Volume 15, Issue 2

ollision C

Volume 15 Issue 2

The International Compendium for Crash Research

Using Civil Twilight Envelopes

To describe commercial trucks’ headlamp illumination

Crashology: Getting It Right

Analysis of a real-world crash with Toyota EDR data, Toyota vehicle control history, and PCS images

Did You Find Any Errors

When you read through the data?

Electric Scooter

Hard brake, acceleration, and top speed testing

3D Delta-V

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Contents

Volume 15 Issue 2

inside 4

Letter from the Editor

5

Collision Magazine Info and Advertiser Index

28

features 28

Using Civil Twilight Envelopes to Describe Commercial Trucks’ Headlamp Illumination

by Jeffrey Andre, April Yergin and Allen Vaughan

40

64

Drag Sled Mechanics: Fundamental Theory and Experimental Verification

by Frank Navin,

54

Crashology: Getting It Right: Analysis of a Real-Word Crash with Toyota EDR Data, Toyota Vehicle Control History, and PCS Images

by Wesley Vandiver and Robert Anderson

64

3D Delta-V

70

Toyota Vehicle Control History: "Sudden Braking History” Recording Characteristics

by Micky Marine 78

by Joseph Teitelman, Eric Rodos, Daniel Wolfe and Michael Helker

78

Electric Scooter Hard Brake, Acceleration and Top Speed Testing

by Robert D Anderson, Michael Rosenfield and Russell Anderson

case study 6

92

Case Study: "Did you find any errors in the data as you read through the data?"

by W. R. Rusty Haight and David W. Sersen

92

Case Study Solution: "The effect of non-planar acceleration captured at the Airbag Control Module on the recorded EDR delta-V data"

by W. R. Rusty Haight and David W. Sersen www.collisionmagazine.com

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Letter from the editor We have all heard it, "the more things change, the more they stay the same." We are almost through 2021; yes, time flies! We have been in, out, and in again with various virus-related restrictions and have witnessed some 20-plus years of development and evolution of the Bosch CDR tool: things change. But there will always be situations where we feel to be out-of-date or out of touch with what's going on. Yes, things stay the same.

By now, we should all recognize that there is a serious "manufacturing and supply chain issue" impacting virtually every aspect of our lives from concerns over whether or not EDR Tool components will be offloaded at some port (then there is the trucking issues to get them to a processing warehouse, which leads to the shortage of employees at the warehouses!) to, when will the new automobile lines start showing up at dealerships to, price increases in nearly every aspect of “things” we purchase. It is exhausting and frustrating.

Recently, there have been some questions regarding the CDR500 FlexRay adapter. Those who resisted purchasing one due to limited coverage; now they need it, but they are not available for purchase (sold out). The CDR500 FlexRay adapter is just one of many EDR tool components that are “out of stock”. Most EDR cables and adapters become more “useful” as time goes on, as they support more vehicles as they are introduced to the market.

When introduced in October of 2014, the CDR500 FlexRay adapter was added to support direct to module imaging for vehicles equipped with airbag ECUs with FlexRay communications. Upon release, the CDR500 adapter was required for imaging EDR data directly from the ECU in the 2014 MINI hardtop, 2015 BMW i3 and i8. Today, with version 21.3 of the CDR Tool software, there is a page in the Help file addressing the use and application of the adapter and lists that many Mercedes, Polestar, Rolls, Audi, Lamborghini, Porsche, VW, Bentley, Toyota, Mini, and Volvo systems require the CDR500 for direct to module (D2M) data retrieval. Things have changed and, in this case, things don't stay the same.

But in many ways, things have remained the same: all too often, some won't or can't look at what's go4 Collision Magazine - Volume 15 Issue 2

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ing on in the bigger picture.

As announced at the Crash Data Group website, several products and components are currently marked as "Sold Out." Because of the headline grabbing disruptions in manufacturing and the supply chain issues affecting product delivery worldwide. Because of the uncertainty surrounding the manufacturing and delivery of almost every product, everywhere, no one really has any sort of time frame for when these items might be back in stock. Of course, this affects the entire EDR Tool product line although the inventory levels are constantly changing and something available today can be sold out tomorrow.

On the Crash Data Group website, where a product is listed as currently "sold out" (out of stock), we have added an application that will notify you by email when the product is back in stock. To use this application, each product page that lists "sold out," also displays a red button that reads "Notify When Available." Click that button, add your email address, and you will be notified the moment that product is back in stock. Of course, when those notification emails from the Crash Data Group are blocked as SPAM, you don't get that notification…well, the more things stay the same.

Sorry to be so long-winded on this topic, this has been my frustration and reality for most of 2021. I would rather be talking about the future of the EDR Summit or other crash related research. Here's to looking forward to 2022 and things NOT staying the same; to things changing back to "normal" (once we figure out what “normal” is).

Scott Baker Managing Editor

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ADVERTISER INDEX

Crash Data Group Inc PO Box 892885, Temecula, CA 92589 Toll Free: 800-280-7940 E-mail: sbaker@crashdatagroup.com https://www.collisionmagazine.com/ ISSN: 1934-8681

COLLISION STAFF Scott Baker Sean Haight Tonya Baker

Owner, Managing Editor Senior Editor Advertising Manager

4N6XPRT Systems CSI Crash Data Group Crash Hub Engineering Dynamics Forensic Mapping Leica Geosystems LightPoint Data Virtual Crash Tesla EDR Tool

PAGE 112, Inside Back 91 62-63, Back Cover 1, 38-39 Inside Front 1 2 53 77 37

SUBMIT AN ARTICLE If you would like to submit an article to be published in Collision please use the following link:

www.collisionmagazine.com/submit BACK ISSUES If you would like purchase back issues of Collision Magazine, please visit

www.collisionmagazine.com/back-issues Most back issues now in digital format!

All rights reserved, © 2021 Crash Data Group Inc. The opinions and conclusions expressed in this publication and any associated data and throughout the articles attributed to specific authors are the opinions and conclusions of the authors noted and not necessarily the editorial staff or anyone else for that matter. While some articles have been reviewed for content, the accuracy of reprinted models or equations cannot be fully guaranteed. It is the responsibility of the reader to apply critical thinking to an individual review of the content and make their own personal judgements as to its value to them, individually.

At the end of the day, facts belong to everybody, any other opinions to us. The distinction is yours to draw...otherwise, the opinions expressed herein are not necessarily those of any employer, not necessarily ours and probably not necessary.

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PR OB LE M:

CA SE

"Did you find any errors in the data as you read through the data?"

W. R. Rusty Haight

Collision Safety Institute

David W. Sersen New York State Police

magine a crash being investigated just a few short years ago, easily within the span of most of our careers, where we were happy – perhaps even excited – to get Event Data Recorder (EDR) data from just one of the involved vehicles. We were all the more excited when it

I

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had more than "just delta-V" data, when it also included Pre-Crash data! In those early days, finding more than one involved vehicle with accessible EDR data which included Pre-Crash data was like finding the pot of gold at the end of the proverbial rainbow. Today, it becomes increasingly

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likely that we'll find that "pot of gold" data set more often as the majority of light trucks, passenger car and SUVs have not only accessible EDR data, but that data has become far more element rich and detailed.

But has it made effectively applying the data really any easier?

This issue's Case Problem comes from a real-world crash, captured in part on video, where both of the involved vehicles have similar, nearly identical data sets. Both are General Motors (GM) vehicles, both are "49CFR563 compliant"1 in terms of the data sets recorded. They are from the same direct to module (D2M) cable family (the "F00K108454" D2M cable family of GM ACMs). The data translation reports for both are almost identical including much of the Data Limitations text. The most significant difference is the actual module variant. One vehicle – the 2013 Buick LaCrosse (Allure) – has what is identified as an "SDM10Pconti" airbag control module (ACM). The other involved

vehicle, a 2017 Chevy Tahoe, has an ACM identified as a "SDM30-delphi."

But it is, in part, some of the similarities found in the data that becomes the basis of this Case Problem.

Background To "set the stage," the police are chasing a Buick LaCrosse.

The why and where isn't terribly important to the Case Problem, in fact, for the Case Problem at hand, that it was a pursuit isn't all that critical except that the other involved vehicle is, as the astute reader might now expect, a police Chevy Tahoe. At a point just before the crash, the deputy driving the Tahoe is aware that the pursuit of the LaCrosse is coming at him, heading his way. He approaches an intersection where he knows the fleeing LaCrosse could make what would be, for the fleeing driver, a left turn or he could go straight, continuing opposite the direction the Tahoe would be then approaching that intersection.

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As the reader might imagine, the deputy pulls up to the intersection slowing as the pursuit approaches in the distance. He's ready to make a right turn and join the chase or a U-turn and fall in line in the chase back in the direction he'd just come from. In the Tahoe's Pre-Crash Data table, we see that, during the last 5 seconds leading up to what the reader no doubt now expects to be the impending crash, the deputy slows through 7 mph (12 km/h) to a stop; his speed is very clearly 0 mph from about the -3.0 sec Pre-Crash Data sample to the -0.5 sec sample: that being last pre-crash sample before "algorithm enable."

After the Tahoe has been fully stopped for about 3 seconds, as anticipated, there's a pretty spectacular crash. For reasons beyond the scope of this Case Problem, the fleeing driver elected to drive the LaCrosse over the centerline in the road and engage the Tahoe fully head on; "hood ornament-to-hood ornament" as it were.

Screen snapshots from a nearby gas station security camera don't fully do the severity of the crash justice as it would be watching in the video itself. Nonetheless, a series of screenshots, captioned with respect to approximate time relative to the impact follow this Case Problem introduction.

Now that the reader has returned from looking for the screen shots to work on the Case Problem, here's the conundrum: on its face, something about the data doesn't "make sense." The question is: "Did you find any errors in the data as you read through the data?"

This seems like the "perfect" crash for an EDR data analyst: both cars have relatively advanced EDR data capabilities, they're both 49CFR563 compliant so there are specific data elements we ARE going to be interested in, and both are, of course, Bosch CDR Tool supported. The crash is documented on video and then very well documented at-scene. We're working with an in-line, single axis crash with one of the two vehicles known, for certain, to be fully stopped. This is effectively that "pot of gold" in terms of the volume and type(s) of EDR data we'd like to have to work with. So, what's the problem, right?

The conundrum All too often, we still hear, even today, of someone finding themselves with EDR data (here from both cars!) and they quickly sort out what page the Pre-Crash Data is on, look there, find the speed of, in this case, the LaCrosse and adopt that as the actual impact speed in the crash. It's: "there, done, easy enough," sign the report and file it.

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it a day – only to find himself later being cross examined about some other aspect of the report, something other than the pre-crash data, and more than likely it's a part of the data that the reconstructionist simply didn't bother to even consider. What happens (has happened) next is the cross examination takes a turn for the worse.

The cross examination takes a deeper dive moving into parts of the data that the reconstructionist never really looked closely at, didn't really consider, didn't check off as "what's most important to me." Sometimes it's as easy as…

Q: In the report, in the System Status at Event data table, there is a data element which reads "Time From Event 1 to 2 (Time Between Events) (seconds)," correct?

A: Yes. Q: And that translated data reads: "Data Not Available," correct?

A: Yes. Q: Was there something wrong with the EDR that makes that data 'not available?'

Now, in a perfect world, the astute observer would catch on that, as is the case here, there was only one recorded event or where this might have been the first occurring event, there wouldn’t be a time between events to report: it would be "not available." Yes, the question is a trap. The question is designed to start the cold sweat on the small of your back and activate that second guessing lobe of the brain. Then the questions get harder…

Q: You described this collision as a full head on frontal collision, correct?

A: Yes. Q: What is an "algorithm?" [cold sweat pumps begin] A: An “algorithm” is a step-by-step procedure or a set of computer program statements to be executed in a specific order to get to a desired output. Here, the desired output is to effectively protect the vehicle occupants with, for example, airbags when they're needed. [/cold sweat] [smug feeling, remembers the slide from the Analyst class verbatim] Q: So, in this full head on front collision, would the LaCrosse's airbag computer run an algorithm associated with the front airbags?

A: Yes, sure.

Q: So, in this full head on, front collision, would the LaCrosse's airbag computer run an algorithm associated with the side airbags?

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Figure 1: The Important Notice common to all Bosch CDR Tool data translation reports A: Possibly, yes. [/smug feeling] [activate second guessing lobe] [cold sweat pumps kick back on] Q: How about a rollover algorithm, in this this full head on front collision, would the LaCrosse's airbag computer start running an algorithm associated with rollover airbags of some sort?

A: (clears throat) Perhaps.

Q: How about an algorithm associated with some sort of 'rear-end protection'? In this crash, that you have described as this full head on front collision, would the LaCrosse's airbag computer start running an algorithm associated with some sort of 'rear-end protection?'

A: No, this is a full head on frontal collision…

the kind of thing that causes an expert's testimony to be limited or excluded altogether. It has happened, and it will happen again.

In this particular case, which algorithm did or did not run isn’t the main concern with the data. Frankly, it is not even a "major concern" but it'll be addressed in the solution.

There is something else, something too many are prone to ignore or dismiss as irrelevant, that is the underlying issue in this Case Problem and does need to be addressed properly up front in a thorough analysis. Which brings us to the fact set and then the assignment.

Facts which may be reliably adopted for the Case Problem 1. The EDR data

[second guessing lobe kicks into overdrive] Q: Let me show you Defendant's exhibit six. This is a portion of, well, it says right here, System Status at Event (Event Record 1) and you have already testified that this represents data associated with this crash, correct?

A: Yes, right. [second guessing lobe kicks into critical alert mode] Q: Let me call your attention to the line near the bottom where it reads "Algorithm Active: Rear" and then, to the right it says "Yes" does it not?

A: (clears throat, long pause) Uh, yes, it does… [cold sweat pumps overloading] What just happened? Well, the good news is that this aspect of the data isn't the real challenge in this Case Problem but the harsh reality is that this sort of line of questions has happened before and will happen again, particularly when we fail to thoroughly review the data – all the data elements in a data translation report, all the elements as translated using the latest version of the CDR Tool software not just those we think are or might be "most important to me."

Then, when we aren’t prepared to answer what seem like obscure questions because they weren't part of "my area of focus," we find our credibility and, best case, our expertise called into question in front of the jury. Worst case, it is

The EDR data retrieved from the two involved vehicles is offered, in a condensed form, over the following pages.

There have been no alterations or changes to the data translation report apart from page formatting to better fit the pages of Collision. Not all of the translated data has been included, again for sake of space (we are talking printed pages, trees, the environment…). All of the data the reader might need to address the questions posed (and perhaps a little more) have been included in the pages in and after this Case Problem statement.

This issue of Collision will have to have been published, put to paper at some point and, at that point, the original CDRx data files will have to be put into a data translation report using the then current Bosch CDR software (Figure 1). That means this issue of Collision will display the translated data as it has been translated using the current version of the CDR Tool software at the time of publishing. Were this to be a litigated case, we all know the best practice, somewhere down the line, would be to open the original raw data files in the latest version of the CDR Tool software at that time and look for potential changes in the data translation report. For now, for this Case Problem, we may assume the data has been presented in the latest version of the software and that translation is reliable as it is presented.

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2. The Lacrosse

The "Assignment"

The involved 2013 Buick LaCrosse was weighed after the crash. The actual weights, by wheel, are: LF: 1000 lbs (454 kg), LR: 800 lbs (363 kg), RR: 750 lbs (340 kg), RF: 1000 lbs (454 kg). The total weight is 3550 lbs. For comparison, the published weight of this vehicle at decodethis.com based on the VIN is listed as 3835 lbs (1610 kg). The "Canadian Specs" ("Winspecs") weight is given as 3766 lbs (1668 kg) with a front/rear weight distribution of 57%/43% (F/R). Given the post crash vehicle condition seen in the photos accompanying this problem statement, one might consider using the average of the high and low end weights which is 3693 lbs (1675 kg). There are a number of photos following this Case Problem statement of the red Buick Lacrosse involved in the actual crash.

As found in a transcript of a pre-trial hearing cross examination of a prosecution's proposed EDR data analysis expert, the defense attorney asked the witness:

3. The Tahoe The 2017 Chevy Tahoe involved in this crash was also weighed after the crash. The actual weights, by wheel are: LF: 1350 lbs (612 kg), LR: 1250 lbs (567 kg), RR: 1350 lbs (612 kg), "RF:" 1600 lbs (725 kg). The right front wheel was no longer actually attached to the Tahoe, so the "RF" weight was taken at the frame member at the back of the right front wheel well. The total weight is 5550 lbs (2517 kg). For comparison, the published weight of this vehicle at media.chevrolet.com is listed as 5683 lbs (2577 kg). The "Canadian Specs" ("Winspecs") weight is given as 5533 lbs (2510 kg), with a front/rear weight distribution of 52%/48% (F/R). Given the vehicle condition post crash, one might consider the average of the high and low end weights which is 5608 lbs (2544 kg). There are a number of photos following this Case Problem statement of the Tahoe involved in the actual crash.

4. The roadway The roadway in the area of the collision is unremarkable.

While the roadway was clear and dry, there was a slight downhill slope in the area of impact and leading away toward the involved vehicle's points of rest. Photos of the scene are found with this Case Problem statement.

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Q: Did you find any errors in the data as you read through the data?

A: No, sir. Not to my knowledge.

Now, one would like to think that, normally, this might prompt the witness to give some thought to why that lawyer might have asked that question? Was there something in the data the witness hadn't noticed that the lawyer thought was an "error" of some sort? Was the lawyer simply "fishing? " Either way, one would like to think it might cause the witness to take a moment after the pretrial hearing and before trial, to open that data file in the latest version of the CDR Tool software and give it a closer, second look (that transcript snippet actually comes from a different case where, unfortunately, that follow up didn't happen.)

Applying that kind of question to this Case Problem, we might be inclined to ask the reader the same thing: Q: Is there anything that seems to be "wrong with the data" you've been presented with?

The reader might consider the LaCrosse data and the Tahoe data and, looking at it all together against what is known about the crash, is there anything that doesn't "look right to you?" To borrow a bit of the line from the lawyer: "Can you find any errors in the data as you read through the data?"

However, for a Case Problem setting, that is a little over broad and doesn't "cut to the chase" to work toward the underlying goal of the solution portion; that being to offer potential solutions or tools to the reader who might find themselves in a similar situation later. So, we will make this a little easier: 1. Think about the concept of force balance here.

Given the average weights of the two vehicles

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as a starting place for your analysis, and the EDR data translation reported delta-V for the LaCrosse, what would the delta-V of the Tahoe have been in this crash?

2. Think about force balance here. Given the average weights of the two vehicles as a starting place for your analysis, and the EDR data translation reported delta-V for the Tahoe, what would the delta-V of the Lacrosse have been in this crash?

These two questions require the reader to (1) find the deltaVs for both vehicles in the EDR data as offered in the Case Problem statement and (2) know how to use the weights of the involved vehicles and delta-V for one of the vehicles to find the delta-V for the other vehicle. This forces the reader to also think about using the delta-V as a part of the larger, more complete analysis rather than simply heading toward the page with the pre-crash data on it…

3. If we adopt the facts as laid out in this problem statement including, for the Case Problem, adopting the average weights of the involved vehicles and the delta-V for the LaCrosse as found in the EDR data translation report, what would the closing speed/velocity for the LaCrosse approaching the Tahoe be at impact?

This requires the reader to use the delta-V from the LaCrosse and the other information provided to calculate the closing speed of the LaCrosse. That then, of course, raises the question: 4. Is the closing speed/velocity for the LaCrosse approaching the Tahoe calculated using the LaCrosse delta-V consistent with the LaCrosse's pre-crash data as it relates to "Speed, Vehicle Indicated" at the last sample before algorithm enable or "time zero?"

That is, of course, calls for a yes or no answer… and, to move the problem along, we'll give the answer to that one here: no, it is not. That allows us to advance the Case Problem to: 5. If we adopt the facts as laid out in this problem statement including, for the Case Problem, adopting the average weights of the involved vehicles and the delta-V for the Tahoe as found in the EDR data translation report, what would the closing speed/velocity for the LaCrosse approaching the Tahoe be at impact?

6. Is the closing speed/velocity for the LaCrosse approaching the Tahoe calculated using the Tahoe delta-V consistent with the LaCrosse's

pre-crash data as it relates to "Speed, Vehicle Indicated" at the last sample before algorithm enable or "time zero?"

By now, the astute reader will have guessed there is probably "something wrong" with the reported delta-V for one of the vehicles. Again, to move the Case Problem along, it would become clear that the "problem data" is the LaCrosse delta-V. So, in that respect, we might follow up with: 7. The delta-V for the LaCrosse is apparently [under-reported] [over-reported] (select one, what did you find?).

8. Given the information available in the Case Problem statement, what should the delta-V of the LaCrosse be?

9. One should be prepared to explain the rationale behind the answer to question #8. In other words: how do you get to your answer for #8? (the wrong answer is: "just because that's what it says in the Pre-Crash data…")

10. What might the reason be for the discrepancy with the LaCrosse delta-V data?

11. Going back to the suggested line of cross-examination questions, where we find "Algorithm Active: Rear" and then, to the right it says "Yes" in the System Status at Deployment data table, for this full overlap frontal crash, why would the rear algorithm be active in this case?

Later in this issue of Collision, we will go through the analysis and offer some answers which, when faced with a similar scenario becomes the source for potential solutions. If one is to take the time to try to sort this out on their own rather than simply go to the solution, the "lesson learned" goal is to demonstrate that sometimes EDR data elements like pre-crash speed are probably just fine, however, there may be something else in the data which doesn't make sense and it needs to be (a) identified as early in the analysis as possible and (b) explained.

Usually, it is best that this explanation be developed before trial.

Footnotes 1. See Title 49 of the Code of Federal Regulations, part 563 (49CFR563), "Event Data Recorder."

See following pages for excerpts from vehicle CDR reports

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Figure 1: Tahoe stopped abt -0.75s pre-impact (T0)

Figure 2: LaCrosse visible abt T0 -0.27s

Figure 5: First frame with the crash, frame +0.0s

Figure 6: About frame time +0.03s

Figure 9: About frame time +0.15s

Figure 10: About frame time +0.21s

Figure 13: About frame time +0.36s

Figure 14: About frame time +0.42s

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Figure 3: About T0 -0.15s

Figure 4: About T0 -0.03s

Figure 7: About frame time +0.09s

Figure 8: About frame time +0.12s

Figure 11: About frame time +0.3s

Figure 12: About frame time +0.33s

Figure 15: About frame time +0.45s

Figure 16: About frame time +0.51s

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Da ta

IMPORTANT NOTICE: Robert Bosch LLC and the manufacturers whose vehicles are accessible using the CDR System urge end users to use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting any retrieved data from within the CDR program. Using the latest version of the CDR software is the best way to ensure that retrieved data has been translated using the most current information provided by the manufacturers of the vehicles supported by this product.

Ta ho e

IMPORTANT Robert Bosch LLC and the manufacturers whose vehicles are accessible using the CDR System urge CDR File NOTICE: Information

end to use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting Userusers Entered VIN 1GNSKDEC6HR****** any Userretrieved data from within the CDR program. Using the latest version of the CDR software is the best way to ensure that retrieved data has been translated using the most current information provided by the manufacturers of the vehicles supported Case Number by thisData product.

EDR Imaging Date

Crash Date Filename TAHOE 1GNSKDEC6HRXXXXXX_ACM.CDRX CDR File Information Saved on User Entered VIN version 1GNSKDEC6HR****** Imaged with CDR Crash Data Retrieval Tool 17.9.1 UserIMPORTANT NOTICE: RoberttoBosch LLC and the manufacturers whose vehicles are accessible CDR urge without Imaged with Software Licensed (Company Company Name information was removedusing whenthe this fileSystem was saved Caseend Number users to use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting Name) VIN sequence number EDRany Data Imaging Date retrieved data from within the CDR program. Using the latest version of Tool the CDR Reported with CDR version Crash Data Retrieval 21.3software is the best way to ensure that Crash Date data has been translated using the most current information provided by the manufacturers of the vehicles supported retrieved Reported with Software Licensed to (Company Filename TAHOE 1GNSKDEC6HRXXXXXX_ACM.CDRX Collision Safety Institute by this product.

Name) Saved on EDR Device Type Airbag Control Module Imaged with CDR version Crash Data Retrieval Tool 17.9.1 Event(s) recovered Deployment CDR File Information Imaged with Software Licensed to (Company Company Name information was removed when this file was saved without

User Entered VIN Name) VIN1GNSKDEC6HR****** sequence number User with CDR version Reported Crash Data Retrieval Tool 21.3 Case Number Comments Reported with Software Licensed to (Company Collision Safety Institute EDR Data Imaging Date Name) No comments entered.

EDR Crash DeviceDate Type Airbag Control Module Filename TAHOE 1GNSKDEC6HRXXXXXX_ACM.CDRX Event(s) recovered Deployment Saved on Imaged with CDR version Crash Data Retrieval Tool 17.9.1 DataImaged Limitations with Software Licensed to (Company Company Name information was removed when this file was saved without Comments Name) VIN sequence number Recorded Crash Events: No comments entered.

Reported with CDR version Crash Data Retrieval Tool 21.3The first is the Non-Deployment Event. A There are two types of recorded crash events for Front, Side, and Rear (FSR) Events.

Reported with Software Licensed (Company Non-Deployment Event records data buttodoes not deploy the air bag(s). The minimum SDM Recorded Vehicle Velocity Change, Collision Safety Institute Name) to record a Non-Deployment Event, is five MPH [8 km/h]. A Non-Deployment Event contains Pre-Crash and Crash that is needed EDRoldest Device Type Airbag Module data. The Non-Deployment event can be overwritten by a Control Deployment Event, if all three records are full and the Nonrecovered Deployment Deployment Event is not locked. A Non-Deployment Event can be overwritten by a more recent Non-Deployment Event if all three DataEvent(s) Limitations records areCrash full and the Non-Deployment is older than approximately 250 ignition cycles. Also, a Non-Deployment event can be Recorded Events: recorded onetypes of theoffollowing withoutfor the Deployment any of the frontal bags, airNon-Deployment bags, or roll bars: There areiftwo recordedoccurs crash events Front, Side, andofRear (FSR) Events.airThe firstside is the Event. A -Pretensioner(s) only data Deployment Comments Non-Deployment Event records but does not deploy the air bag(s). The minimum SDM Recorded Vehicle Velocity Change, -Head Deployment that isNo needed to Rest record a Non-Deployment Event, is five MPH [8 km/h]. A Non-Deployment Event contains Pre-Crash and Crash comments entered.

Cut-Off Deployment data. The-Battery oldest Non-Deployment event can be overwritten by a Deployment Event, if all three records are full and the NonThe second type ofisSDM recorded event for FSR Events is the Deployment Event.

It also contains Pre-Crash Deployment Event not locked.

A crash Non-Deployment Event can be overwritten by a more recent Non-Deployment Eventand if allCrash three data.

Deployment Events cannot be overwritten cleared the SDM. 250 ignition cycles. Also, a Non-Deployment event can be records are full and the Non-Deployment is or older than by approximately Rollover Events contains Pre-Crash and Crash data.

Rollover event follow the same rules as FSR Deployment events. recorded if one of the following occurs without the Deployment of any of the frontal air bags, side air bags, or roll bars: Data Limitations The SDM can store up to three Events.

-Pretensioner(s) only Deployment Recorded Crash Events: -Head Rest Deployment There are two types of recorded crash events for Front, Side, and Rear (FSR) Events. The first is the Non-Deployment Event. A -Battery Cut-Off Deployment Data: Non-Deployment Event records data but does not deploy the air bag(s). The minimum SDM Recorded Vehicle Velocity Change, TheFSR second type SDM of to SDM recorded crash event for FSR is the Deployment also contains Pre-Crash andand Crash data.

For Recorded Vehicle Velocity Change the change inEvent. velocityIt that the sensing system experienced thatEvents, is needed record a Non-Deployment Event, is Events five reflects MPH [8 km/h].

A Non-Deployment Event contains Pre-Crash Crash Deployment Events cannot be overwritten or can cleared by theVehicle SDM. during the recorded portion of the event.

SDM Recorded Change is ifthe change in velocity the recording time data.

The oldest Non-Deployment event be overwritten by aVelocity Deployment Event, all three records are fullduring and the NonRollover contains Pre-Crash Crash before data. Rollover event rules FSRNon-Deployment Deployment and isDeployment notEvents the speed theisvehicle wasand traveling the event and is alsothe notsame the Equivalent Velocity.events.

For Deployment Event not locked.

A Non-Deployment Event can befollow overwritten by aBarrier more as recent Event if all three and The SDM canare store up three Events.

Non-Deployment Events, theNon-Deployment SDM will record to than 300 milliseconds data aftercycles. time zero.

SDM will also record up be to 300 records full andtothe is up older approximatelyof 250 ignition Also,The a Non-Deployment event can recorded one of the following occurs without thezero.

Deployment of any of the frontal air bags, side air bags, or roll bars: milliseconds ofifVehicle Acceleration data after time Data: -Pretensioner(s) only Deployment For Rollover Events, the SDM may record Lateral Acceleration, Vertical Acceleration, and Roll Rate data, if the SDM is rollover For FSR Events, SDM Recorded Vehicle Velocity Change reflects the change velocity portion that theofsensing system -Head Rest Deployment capable.

This data reflects what the sensing system experienced during the in recorded the event.

Forexperienced Rollover Deployment during the ofupthe SDM Recorded Vehicle Velocity Change is criteria the change in and velocity the recording time -Battery Cut-Off Deployment Events, therecorded SDM willportion record to event.

700 milliseconds of data before the Deployment is met 290during milliseconds after the type of SDM recorded crash event for the FSRevent Events the Deployment Event.Equivalent It also contains Pre-Crash and Crash data. and isThe notsecond the speed vehicle was traveling before andis is also not the Barrier Velocity.

For Deployment and Deployment criteria isthe met.

Deployment Events cannot be overwritten or cleared by the SDM. Non-Deployment Events, the SDM willas record to 300 milliseconds of data afterevent time zero. will also event recordisupqualified to 300 during -Deployment loops may be displayed beingupdeployed in a Non-Deployment record,The if aSDM Deployment RolloverofEvents contains Pre-Crash and Crash Rollover event follow the same rules as FSR Deployment events. milliseconds Vehicle Acceleration data timedata. zero. the Non-Deployment event. is,Events. if twoafter or more events are occurring at the same time and one is a Non-Deployment event and The SDM can store up toThat three For Rollover Events, the SDM may record Lateral Acceleration, Vertical Acceleration, and Roll Rate data, if the SDM is rollover one of the others is a Deployment event, and the Deployment event is qualified while the Non-Deployment is still active, the capable. This data reflects what the sensing system experienced during the recorded portion of the event. For Rollover Deployment deployed loops may be recorded in the Non-Deployment event record.

Data: Events, the SDM will record up to 700 milliseconds of data before the Deployment criteria is met and 290 milliseconds after the -Time For between eventsSDM is recorded in 10 msec intervals and is displayed in seconds for a maximum time of 655.33 seconds. The FSRcriteria Events, Deployment is met. Recorded Vehicle Velocity Change reflects the change in velocity that the sensing system experienced counter measures the time fromofthe of one event to theVehicle start ofVelocity the next event if the both eventsin occur within the same ignition during the recorded portion thestart event.

Recorded Change change velocity during recording time -Deployment loops may be displayed as beingSDM deployed in a Non-Deployment event is record, if a Deployment event the is qualified during cycle. and is not the speed the That vehicle theare event and is at also the Barrier Equivalent For Deployment and the Non-Deployment event. is, was if twotraveling or morebefore events occurring thenot same time and one is aVelocity.

Non-Deployment event and Non-Deployment Events, the SDM will record up to 300 milliseconds of data after time zero.

The SDM will also record up to 300 -The Maximum SDM Vehicle mayevent occurisbetween recorded 10 millisecondissample points one of the others is a Recorded Deployment event,Velocity and the Change Deployment qualified the while the Non-Deployment still active, the of the SDM milliseconds of VehicleChange.

Acceleration data after zero.

Recorded Vehicle Velocity The SDM willtime only record Maximum SDM Recorded Vehicle Velocity Change for the first 300 deployed loops may be recorded in the Non-Deployment event record.

For Rollover Events, the SDM may record Lateral Acceleration, Vertical Acceleration, and Roll Rate data, if the SDM is rollover milliseconds of the event.

-Time between events is recorded in 10 msec intervals and is displayed in seconds for a maximum time of 655.33 seconds. The This data reflectsVelocity what theChange sensing data system experienced during the recorded of thethen event.

Rollovervalues Deployment -If the capable.

SDM Recorded Vehicle theofmax output range ofportion -127 km/h the For exceeded will be counter measures the will time from the start one eventexceeds to the event if criteria both events occur within the sameafter ignition Events, upkm/h. to 700of of the datastart before the next Deployment is met and 290 milliseconds the of +126 displayed withthe anSDM offset ofrecord a +256 Ifmilliseconds the SDM Recorded Vehicle Velocity Change data exceeds the max output range cycle.Deployment criteria is met.

1GNSKDEC6HR****** Page of 29 km/h. on: Sunday, October 31of2021 at 09:44:47 km/h the exceeded values will be displayed with an may offset of a1between -256 -The then Maximum SDM Recorded Vehicle Velocity Change occur the event recorded 10Printed sample points the during SDM -Deployment loops may be displayed as being deployed in a Non-Deployment record, ifmillisecond a Deployment event is qualified Recorded VehicleComplete Velocityevent.

Change. will only record Maximum Recorded Vehicle Change fororthe -Event Recording will indicate dataor from the recorded event SDM has been fullytime written toVelocity the memory ifevent itfirst has300 been the Non-Deployment ThatThe is, ififSDM two more events are occurring at the same and one is aSDM Non-Deployment and milliseconds of not the event. interrupted fullyis written. one ofand the others a Deployment event, and the Deployment event is qualified while the Non-Deployment is still active, the -If thedeployed SDM Recorded Vehicle Velocity data exceeds the max output range of -127 then the exceeded values will be -SDM Recorded Vehicle Speed accuracy be affected byevent various factors, including but km/h not limited to the following: loops may be recorded inChange thecan Non-Deployment record. displayed with an offset of ais+256 km/h.

If the SDM Recorded Vehicle Velocity Change exceedstime theofmax output range The of +126 -Significant changes in the tire’s rolling radius -Time between events recorded in 10 msec intervals and is displayed in seconds fordata a maximum 655.33 seconds. counter measures the ratio time from the start of one event to the start the next event if bothPrinted eventson: occur within the same ignition -Final drive axle changes 1GNSKDEC6HR****** Page 1 ofof29 Sunday, October 31 2021 at 09:44:47 cycle.

-Wheel lockup and wheel slip Maximum Recorded Vehicle Velocity Change occur between the recorded 10 millisecond sample points of the SDM -Brake-The Switch CircuitSDM Status indicates the open/closed statemay of the brake switch circuit.

Recorded Velocity Change. The SDM record Maximumdata SDM Recorded Change for theisfirst -Pre-Crash dataVehicle is recorded asynchronously.

Thewill 0.5only second Pre-crash value (mostVehicle recent Velocity recorded data point) the300 data milliseconds the event. point last sampledofbefore Time Zero. That is to say, the last data point may have been captured just before Time Zero but no more -If the SDMbefore Recorded Vehicle Change data exceeds the values max output range of -127 km/h then thepoint. exceeded values will be than 0.5 second Time Zero.Velocity All subsequent Pre-crash data are referenced from this data displayed with anData offsetValidity of a +256 km/h.Status If the SDM Recorded Change data exceeds the max output range of +126 -Pre-Crash Electronic Check indicates “DataVehicle Invalid”Velocity if: 1GNSKDEC6HR****** Page 1the of 29 on: Sunday, -The SDM receives a message with an “invalid” flag from module sending thePrinted pre-crash data October 31 2021 at 09:44:47 -Pre-Crash Electronic Data Validity Check Status indicates “Data Not Available” if: -No data is received from the module sending the pre-crash data -For diesel powered vehicles, the data displayed as Throttle Position (%) is actually the data for the Air Inlet Flap Position. This is not the same as the throttle position for a gasoline powered engine.

-Belt Switch Circuit Status indicates the status of the seat belt switch circuit.

-The ignition cycle counter will increment when the power mode cycles from OFF/Accessory to RUN. Applying and removing of battery power to the module will not increment the ignition cycle counter.

-Ignition Cycles Since DTCs Were Last Cleared can record a maximum value of 253 cycles and can only be reset by a scan tool.

-Dynamic Deployment Event Counter tracks the number of Deployment events that have occurred during the SDM’s lifetime.

-Dynamic Event Counter tracks the number of qualified events (either Deployments, Non-deploy, or Rollover events) that have occurred during the SDM’s lifetime.

-For Deployment Events, DTC B0052 (Deployment commanded) shall be recorded with the remainder of the data for this event even though it occurred after Event Enable.

-Once a firing loop has been commanded to be deployed, it will not be commanded to be deployed again during the same ignition cycle. Firing loop times for subsequent deployment type events, during the same ignition cycle, will record the deployment times as N/A. 14 Collision Magazine 15 Issue www.collisionmagazine.com -In an- Volume event where the2 module is operating on energy reserve, the Dynamic counters may report a value that is less than the actual value. If the stored values in the Dynamic counters are less than the counter values in the event records or if more than one event record has the same counter value as another, the module may have been operating on its energy reserve.

-A Concurrent Event is when two events are happening nearly simultaneously. The “Concurrent Event Flag Set” parameter will indicate “Yes” if one event begins, but before that event is qualified, another event begins and is qualified.

A Non-Deployment event typically becomes qualified if that event exceeds the 5 MPH (8 km/h) delta V recording threshold and the Volume 15 Issue 2.indd 14 event has concluded. A deployment event (FSR or Rollover) becomes qualified when a deployment has been commanded for that event.

11/18/2021 9:22:39 AM

-The SDM receives a message with an “invalid” flag from the module sending the pre-crash data -Belt Switch Circuit Status indicates the status of the seat belt switch circuit.

-Pre-Crash Electronic Data Validity Check Status indicates “Data Not Available” if: -The ignition cycle counter will increment when the power mode cycles from OFF/Accessory to RUN. Applying and removing of -No data is received from the module sending the pre-crash data battery power to the module will not increment the ignition cycle counter.

-For diesel powered vehicles, the data displayed as Throttle Position (%) is actually the data for the Air Inlet Flap Position. This is -Ignition Cycles Since DTCs Were Last Cleared can record a maximum value of 253 cycles and can only be reset by a scan tool. not the same as the throttle position for a gasoline powered engine.

-Dynamic Deployment Event Counter tracks the number of Deployment events that have occurred during the SDM’s lifetime.

-Belt Switch Circuit Status indicates the status of the seat belt switch circuit.

-Dynamic Event Counter tracks the number of qualified events (either Deployments, Non-deploy, or Rollover events) that have -The ignition cycle counter will increment when the power mode cycles from OFF/Accessory to RUN. Applying and removing of occurred during the SDM’s lifetime. battery power to the module will not increment the ignition cycle counter.

-For Deployment Events, DTC B0052 (Deployment commanded) shall be recorded with the remainder of the data for this event even -Ignition Cycles Since DTCs Were Last Cleared can record a maximum value of 253 cycles and can only be reset by a scan tool. though it occurred after Event Enable.

-Dynamic Deployment Event Counter tracks the number of Deployment events that have occurred during the SDM’s lifetime.

-Once a firing loop has been commanded to be deployed, it will not be commanded to be deployed again during the same ignition -Dynamic Event Counter tracks the number of qualified events (either Deployments, Non-deploy, or Rollover events) that have cycle. Firing loop times for subsequent deployment type events, during the same ignition cycle, will record the deployment times as occurred during the SDM’s lifetime.

N/A. -For Deployment Events, DTC B0052 (Deployment commanded) shall be recorded with the remainder of the data for this event even -In an event where the module is operating on energy reserve, the Dynamic counters may report a value that is less than the actual though it occurred after Event Enable. value. If the stored values in the Dynamic counters are less than the counter values in the event records or if more than one event -Once a firing loop has been commanded to be deployed, it will not be commanded to be deployed again during the same ignition record has the same counter value as another, the module may have been operating on its energy reserve. cycle. Firing loop times for subsequent deployment type events, during the same ignition cycle, will record the deployment times as -A Concurrent Event is when two events are happening nearly simultaneously. The “Concurrent Event Flag Set” parameter will N/A. indicate “Yes” if one event begins, but before that event is qualified, another event begins and is qualified.

-In an event where the module is operating on energy reserve, the Dynamic counters may report a value that is less than the actual A Non-Deployment event typically becomes qualified if that event exceeds the 5 MPH (8 km/h) delta V recording threshold and the value. If the stored values in the Dynamic counters are less than the counter values in the event records or if more than one event event has concluded. A deployment event (FSR or Rollover) becomes qualified when a deployment has been commanded for that record has the same counter value as another, the module may have been operating on its energy reserve. event.

-A Concurrent Event is when two events are happening nearly simultaneously. The “Concurrent Event Flag Set” parameter will Example of a Concurrent Event: indicate “Yes” if one event begins, but before that event is qualified, another event begins and is qualified.

A Rollover event begins. Before the Rollover event is qualified, a Non-Deployment event begins and is qualified. Sometime after A Non-Deployment event typically becomes qualified if that event exceeds the 5 MPH (8 km/h) delta V recording threshold and the the Non-Deployment event is qualified, the Rollover event is qualified. The Non-Deployment event will be recorded in the first open event has concluded. A deployment event (FSR or Rollover) becomes qualified when a deployment has been commanded for that record even though the Rollover event enabled before the Non-Deployment event. The Rollover event will be recorded in the next event. open record. The “Concurrent Event Flag Set” parameter will indicate “Yes” for the Rollover event. The “Time Between Events” Example of a Concurrent Event: parameter will indicate the time from the start of the Rollover event to the start of the Non-Deployment event.

A Rollover event begins. Before the Rollover event is qualified, a Non-Deployment event begins and is qualified. Sometime after the Non-Deployment event is qualified, the Rollover event is qualified. The Non-Deployment event will be recorded in the first open record even though the Rollover event enabled before the Non-Deployment event. The Rollover event will be recorded in the next open record. The “Concurrent Event Flag Set” parameter will indicate “Yes” for the Rollover event. The “Time Between Events” parameter will indicate the time from the start of the Rollover event to the start of the Non-Deployment event.

-The GM parameter name is displayed in parentheses after the NHTSA Part 563 parameter name.

1GNSKDEC6HR******

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1GNSKDEC6HR******

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-The reported range of the longitudinal and lateral acceleration values is approximately ± 50 g.

-Due to a CDR Tool data imaging issue, all CDR files imaged from SDM-30 Delphi airbag control modules (ACM) using version 17.6 software are invalid and the ACM must be re-imaged using CDR version 17.6.1 and later software.

-The GM should parameter name is displayed in parentheses after the NHTSA 563 parameter name.and scene.

-All data be examined in conjunction with other available physical Part evidence from the vehicle Data Source: All SDM recorded data is measured, calculated, and stored internally, except for the following: -Vehicle Status Data (Pre-Crash) is transmitted by the Body Control Module, via the vehicle’s communication network.

-The Belt Switch Circuit is wired directly to the SDM.

Data Element Sign Convention: The following table provides an explanation of the sign notation for data elements that may be included in this CDR report.

Directional references to sign notation are all from the perspective of the driver when seated in the vehicle facing the direction of forward vehicle travel.

Data Element Name Longitudinal Acceleration Longitudinal Velocity Change Lateral Acceleration Lateral Velocity Change Vertical Acceleration Roll Rate

Positive Sign Notation Indicates Forward Forward Left to Right Left to Right Downward Clockwise Rotation

Hexadecimal Data: Data that the vehicle manufacturer has specified for data retrieval is shown in the hexadecimal data section of the CDR report. The hexadecimal data section of the CDR report may contain data that is not translated by the CDR program. The control module contains additional data that is not retrievable by the CDR tool.

01050_SDM30-delphi_r020

System Status at Time of Retrieval

Volume 15 Issue 2.indd 15

Dynamic Deployment Event Counter Multi-Event, Number of Events (Dynamic Event Counter) Dynamic OnStar Notification Event Counter Vehicle Identification Number (VIN) Ignition Cycle, Download (Ignition Cycles at Investigation) End Model Part Number System Type Software Module Identifier 1 Software Module Identifier 2 Software Module Identifier 3 Manufacturing Traceability Data, LineID Manufacturing Traceability Data, ShiftID Manufacturing Traceability Data, Year Manufacturing Traceability Data, DayOfTheYear www.collisionmagazine.com Manufacturing Traceability Data, Serial/Lot/BatchNumber ESS # 1 Traceability Data, Component Identifier ESS # 1 Traceability Data, Part Number/Broadcast Code ESS # 1 Traceability Data, Supplier Code ESS # 1 Traceability Data, Traceability Number ESS # 2 Traceability Data, Component Identifier ESS # 2 Traceability Data, Part Number/Broadcast Code

1 1 1 1GNSKDEC6HR****** 786 00CE44C3 Delphi with integrated IMU 00CE44C1 05051747 01621D42 K 1 17 072 Collision Magazine - Volume I5 Issue 2 15 3M306ZM00 AU 8677 D P00000000 AT 11/18/2021 9:22:39 AM 8677

System Status at Event (Event Record 1)

Event Record Type OnStar Deployment Status Data Sent Complete file recorded (Event Recording Complete) Crash Record Locked OnStar SDM Recorded Vehicle Velocity Change Data Sent Deployment Event Counter Multi-Event, Number of Events (Event Counter) OnStar Notification Event Counter Time From Event 1 to 2 (Time Between Events) (seconds) Ignition Cycle, Crash (Ignition Cycles at Event) Algorithm Active: Frontal Algorithm Active: Side Algorithm Active: Rollover Algorithm Active: Rear Concurrent Event Flag Set Event Severity Status: Frontal Pretensioner Event Severity Status: Frontal Stage 1 Event Severity Status: Frontal Stage 2 Event Severity Status: Left Side Event Severity Status: Right Side Event Severity Status: Rear Event Severity Status: Rollover Safety Belt Status, Driver (Driver Belt Switch Circuit Status) Safety Belt Status, Right Front Passenger (Passenger Belt Switch Circuit Status) Center Front Row Belt Switch Circuit Status (If Equipped) Left Row 3 Belt Switch Circuit Status (If Equipped) Center Row 3 Belt Switch Circuit Status (If Equipped) Right Row 3 Belt Switch Circuit Status (If Equipped) Seat Track Position Switch, Foremost, Status, Driver (Driver Seat Position Status) Passenger Seat Occupancy Status Occupant Size Right Front Passenger Child (Passenger Classification Status) Passenger Air Bag ON Indicator Status Passenger Air Bag OFF Indicator Status Low Tire Pressure Warning Lamp Status 0.5 Seconds Prior to Time Zero Frontal Air Bag Warning Lamp (SIR Warning Lamp Status 0.5 Seconds Prior to Time Zero) SIR Warning Lamp ON/OFF Time Continuously (seconds) Number of Ignition Cycles SIR Warning Lamp was ON/OFF Continuously Ignition Cycles Since DTCs Were Last Cleared 0.5 Seconds Prior to Time Zero Maximum Delta-V, Longitudinal (Maximum Longitudinal SDM Recorded Vehicle Velocity Change for FSR Event) MPH [km/h] Time, Maximum Delta-V (Time From FSR Time Zero to Maximum Longitudinal SDM Recorded Vehicle Velocity Change)(msec) Maximum Delta-V, Lateral (Maximum Lateral SDM Recorded Vehicle Velocity Change for FSR Event) MPH [km/h] Time Maximum Delta-V, Lateral (Time From FSR Time Zero to Maximum Lateral SDM Recorded Vehicle Velocity Change)(msec) High Voltage Disable Notification Sent Deployment Commanded in Energy Reserve Mode

Deployment Yes Yes Yes Yes 1 1 1 Data Not Available 784 Yes Yes Yes Yes No Yes Yes Yes No No No No Buckled Not Buckled Data Not Available Data Not Available Data Not Available Data Not Available No (Rearward) Empty No (Not Applicable) Off On Off Off 655330 760 253 -48 [-77] 104 -1 [-2] 76 Yes No

Event Data (Event Record 1)

Driver 1st Stage Deployment Loop Commanded Yes Passenger 1st Stage Deployment Loop Commanded No Driver 2nd Stage Deployment Loop Commanded Yes Passenger 2nd Stage Deployment Loop Commanded No Driver Pretensioner Deployment Loop #1 Commanded Yes Passenger Pretensioner Deployment Loop #1 Commanded Yes Driver Pretensioner Deployment Loop #2 Commanded Yes Passenger Pretensioner Deployment Loop #2 Commanded Yes Driver Thorax Loop Commanded No Passenger Thorax Loop Commanded No Left Row 1 Roof Rail/Head Curtain Loop Commanded Yes Right Row 1 Roof Rail/Head Curtain Loop Commanded Yes Frontal Air Bag Deployment, Time to 1st Stage Deployment, Driver (Driver 1st Stage 13 Time From Time Zero to Deployment Command Criteria Met) (msec) 1GNSKDEC6HR****** Page 5 of 29 Printed on: Sunday, October 31 2021 at 09:44:47 Frontal Air Bag Deployment, Time to 2nd Stage, Driver (Driver 2nd Stage Time From 16 Time Zero to Deployment Command Criteria Met) (msec) Frontal Air Bag Deployment, Time to 1st Stage Deployment, Right Front Passenger (Passenger 1st Stage Time From Time Zero to Deployment Command Criteria Met) Data Not Available (msec) Frontal Air Bag Deployment, Time to 2nd Stage, Right Front Passenger (Passenger Data Not Available 2nd Stage Time From Time Zero to Deployment Command Criteria Met) (msec) Side air bag deployment, time to deploy, driver (Driver Thorax/Curtain Time From 13 Time Zero to Deployment Command Criteria Met) (msec) Side air bag deployment, time to deploy, right front passenger (Passenger 13 Thorax/Curtain Time From Time Zero to Deployment Command Criteria Met) (msec) Pretensioner Deployment, Time to Fire, Driver (Driver Pretensioner Time From Time 13 Zero to Deployment Loop #1 or Loop #2 Command Criteria Met) (msec) Pretensioner Deployment, Time to Fire, Right Front Passenger (Passenger Pretensioner Time From Time Zero to Deployment Loop #1 or Loop #2 Command 13 Criteria Met) (msec)

16 Collision Magazine - Volume 15 Issue 2

Volume 15 Issue 2.indd 16

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11/18/2021 9:22:39 AM

Longitudinal Crash Pulse (Event Record 1)

Lateral Crash Pulse (Event Record 1)

Delta-V, Longitudinal (SDM Recorded Vehicle Longitudinal Velocity Change for FSR Event)

80

Delta-V, Lateral (SDM Recorded Vehicle Lateral Velocity Change for FSR Event)

40

120 70

60

110 100

60

90

50

40

80 70

40

20

60

30

50

30

20

40 20

10

30

10

-10

-10

k m /h

0

0

0 -10

-20 -30

-20

-10 -20

-40 -30

-50 -60

-40

-40

-80 -90

-60

-30

-100

-50

-110

-70

-60

-120 0

20

40

60

80

100

120

140

msec

160

180

200

220

240

260

280

-40

300

Longitudinal Acceleration (SDM Recorded Vehicle Longitudinal Acceleration for FSR Event)

60

0

20

40

60

80

100

120

140

msec

160

180

200

220

240

260

280

200

220

240

260

280

300

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event)

60

50

50

40

40

30

30

20

20

10

g

10

g

-30

-20

-70

-50

-80

k m /h

10

0

MPH

20

10

MPH

50

30

0

0

-10

-10

-20

-20

-30

-30 -40

-40 -50

-50 -60

-60

0

20

40

60

80

100

1GNSKDEC6HR******

120

140

msec

160

180

200

220

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240

260

280

0

20

40

60

80

100

300

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Longitudinal Crash Pulse (Event Record 1)

1GNSKDEC6HR******

120

140

msec

160

180

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300

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Lateral Crash Pulse (Event Record 1)

Time (msec)

Delta-V, Longitudinal (SDM Recorded Vehicle Longitudinal Velocity Change for FSR Event) (MPH)

Delta-V, Longitudinal (SDM Recorded Vehicle Longitudinal Velocity Change for FSR Event) (km/h)

Time (msec)

Delta-V, Lateral (SDM Recorded Vehicle Lateral Velocity Change for FSR Event) (MPH)

Delta-V, Lateral (SDM Recorded Vehicle Lateral Velocity Change for FSR Event) (km/h)

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300

-1.9 -6.8 -12.4 -20.5 -31.7 -39.8 -44.7 -46.6 -47.2 -47.2 -47.2 -47.2 -47.2 -46.6 -46.6 -46.0 -45.4 -45.4 -45.4 -44.7 -44.7 -44.7 -44.7 -44.7 -44.7 -45.4 -45.4 -45.4 -45.4 -45.4

-3.0 -11.0 -20.0 -33.0 -51.0 -64.0 -72.0 -75.0 -76.0 -76.0 -76.0 -76.0 -76.0 -75.0 -75.0 -74.0 -73.0 -73.0 -73.0 -72.0 -72.0 -72.0 -72.0 -72.0 -72.0 -73.0 -73.0 -73.0 -73.0 -73.0

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300

0.0 0.0 0.6 0.0 0.0 0.6 -0.6 -1.2 -0.6 -0.6 0.0 -0.6 -0.6 -0.6 -0.6 -0.6 0.0 0.0 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6

0.0 0.0 1.0 0.0 0.0 1.0 -1.0 -2.0 -1.0 -1.0 0.0 -1.0 -1.0 -1.0 -1.0 -1.0 0.0 0.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0 -1.0

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Longitudinal Crash Pulse (Event Record 1) Time (msec)

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100

Longitudinal Acceleration (SDM Recorded Vehicle Longitudinal Acceleration for FSR Event) (g) -1.4 -3.8 -7.0 -12.6 -14.6 -19.4 -17.0 -17.4 -24.6 -23.4 -24.2 -23.8 -25.0 -26.2 -27.4 -41.8 -51.0 -35.0 -39.4 -15.4 -37.0 -51.0 -35.8 -39.0 -51.0 -42.6 -42.6 -39.4 -32.6 -33.8 -33.4 -31.4 -19.8 -24.2 -19.8 -9.4 -10.2 -4.6 -6.2 -6.6 -10.6 -6.2 -5.8 0.6 0.2 -1.0 -3.8 0.2 -0.2 -2.2

Rollover Crash Pulse (Event Record 1) SDM Recorded Vehicle Roll Rate 1GNSKDEC6HR****** Contains No Recorded Data

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Time (msec)

102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200

Longitudinal Time Longitudinal Acceleration (msec) Acceleration (SDM Recorded Vehicle (SDM Recorded Vehicle Longitudinal Longitudinal Acceleration for FSR Acceleration for FSR Event) (g) Event) (g) -1.4 202 -0.2 -0.6 204 -0.2 0.2 206 -0.6 3.4 208 -0.2 -2.2 210 -0.2 -0.2 212 0.6 2.6 214 -0.2 -0.2 216 -0.6 1.8 218 -0.6 0.2 220 -0.6 -0.6 222 -0.6 -1.0 224 -0.2 -0.6 226 -0.6 0.2 228 -0.2 1.8 230 -0.2 3.4 232 -0.2 1.8 234 -0.2 -0.6 236 -0.2 0.2 238 -0.2 -0.2 240 0.2 1.4 242 -0.2 0.6 244 -0.2 1.8 246 -0.2 1.8 248 -0.2 1.4 250 -0.6 2.2 252 -0.6 1.4 254 -0.6 2.2 256 -0.6 1.8 258 -0.2 2.6 260 -0.2 1.8 262 -0.6 2.6 264 -0.6 3.0 266 -0.2 268 -0.2 Rollover Crash1.8 Pulse (Event Record 1) SDM Recorded2.2 Vehicle Roll270 Rate -0.2 1.8 272 0.2 Contains No Recorded Data 0.6 274 -0.2 1.8 276 -0.2 -0.2 278 -0.2 1.8 280 -0.2 0.6 282 -0.6 0.2 284 -0.2 0.2 286 -0.2 -0.2 288 -0.6 0.2 290 -0.2 1.0 292 -0.2 0.2 294 -0.2 0.6 296 -0.2 0.6 298 -0.2 0.2 300 -0.2 Rollover Crash Pulse (Event Record 1) Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for Rollover Event) Page 10 of 29 Printed on: Sunday, October 31 2021 at 09:44:47 Contains No Recorded Data

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Lateral Crash Pulse (Event Record 1) Time (msec)

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event) (g)

Time (msec)

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event) (g)

Time (msec)

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event) (g)

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100

-0.2 -0.6 -0.2 -0.6 0.2 -0.6 -1.0 1.8 1.8 0.6 8.6 -3.0 -3.4 0.2 1.8 0.6 -1.0 -11.0 6.6 -11.4 9.4 12.2 -6.6 1.0 -5.0 -19.8 17.8 3.8 -8.6 11.8 5.8 0.6 -7.4 -7.4 -10.2 -13.8 0.2 -4.2 -1.0 2.6 -1.0 3.4 -0.6 1.4 3.0 -1.0 1.8 1.8 2.6 0.2

102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200

3.8 3.0 1.0 -1.8 -1.0 0.2 -1.4 -3.4 -1.4 -2.2 -2.6 -0.2 -0.6 -3.0 -2.6 0.6 -1.8 -1.4 2.2 0.2 0.2 -1.8 0.6 -1.8 -0.6 1.8 0.6 -0.6 0.6 0.6 1.4 1.0 2.2 1.8 1.4 1.8 1.4 -0.2 -1.8 0.2 -1.4 -1.0 -0.6 -1.8 -1.0 -1.0 -0.6 0.2 0.2 -1.0

202 204 206 208 210 212 214 216 218 220 222 224 226 228 230 232 234 236 238 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268 270 272 274 276 278 280 282 284 286 288 290 292 294 296 298 300

-0.6 -0.6 0.2 -0.6 -0.6 0.6 -0.6 -0.6 0.2 -0.6 -0.6 -0.6 -0.2 -0.6 -1.0 0.2 -0.2 -1.0 0.2 0.2 -0.2 -0.2 0.2 0.6 -0.6 0.2 0.2 0.2 -0.2 -0.2 0.2 -0.2 -0.2 -0.6 0.6 -0.6 -1.0 -0.2 -0.2 -0.6 -0.6 -0.2 0.2 -1.0 0.2 -0.2 -0.2 -0.2 0.2 0.2

Vertical Crash Pulse (Event Record 1) Normal Acceleration (SDM Recorded Vehicle Vertical Acceleration for Rollover 1GNSKDEC6HR****** Page 13 of 29 Event)

Printed on: Sunday, October 31 2021 at 09:44:47

Contains No Recorded Data

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Pre-Crash Data -5.0 to -0.5 sec (Event Record 1) Times (sec) -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5

Accelerator Pedal, % Full (Accelerator Pedal Position) 0 0 0 0 0 0 0 0 0 0

Service Brake (Brake Switch Circuit State)

Engine RPM (Engine Speed)

Engine Throttle, % Full (Throttle Position)

Speed, Vehicle Indicated (Vehicle Speed) (MPH [km/h])

On On On On On On On On On On

704 704 704 576 576 576 512 512 512 512

7 8 9 8 8 8 7 7 7 7

7 [ 12] 6 [ 10] 4 [ 7] 2 [ 3] 0 [ 0] 0 [ 0] 0 [ 0] 0 [ 0] 0 [ 0] 0 [ 0]

Pre-Crash Data -2.0 to -0.5 sec (Event Record 1) Times (sec)

Cruise Control Active

Cruise Control Resume Switch Active

Cruise Control Set Switch Active

Engine Torque (lb-ft [N-m])

Reduced Engine Power Mode Indicator

-2.0 -1.5 -1.0 -0.5

No No No No

No No No No

No No No No

3 [ 4] 3 [ 4] 6 [ 8] 4 [ 6]

Off Off Off Off

1GNSKDEC6HR******

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IMPORTANT NOTICE: Robert Bosch LLC and the manufacturers whose vehicles are accessible using the CDR System urge end users to use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting any retrieved data from within the CDR program. Using the latest version of the CDR software is the best way to ensure that retrieved data has been translated using the most current information provided by the manufacturers of the vehicles supported by this product.

se os cr La

IMPORTANT NOTICE: Robert Bosch LLC and the manufacturers whose vehicles are accessible using the CDR System urge end users to use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting CDR File Information any retrieved the latest version of the CDR software is the best way to ensure that User Entereddata VINfrom within the CDR program. Using1G4GC5ER5DF****** retrieved data has been translated using the most current information provided by the manufacturers of the vehicles supported User by this Number product.

Case EDR Data Imaging Date Crash Date

CDR File Information Filename

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Volume 15 Issue 2.indd 21

ta Da

BUICK 1G4GC5ER5DFXXXXXX_ACM.CDRX User Entered VIN 1G4GC5ER5DF****** Saved on IMPORTANT NOTICE: Robert Bosch LLC and the manufacturers vehicles are accessible using the CDR System urge User Imaged with CDR version Crash Data whose Retrieval Tool 17.9.1 end users to use the latest production release of the Crash Data Name Retrieval system software when when viewing, exporting Case Number Imaged with Software Licensed to (Company Company information was removed thisprinting file wasorsaved without any retrieved data from within the CDR program. UsingVIN thesequence latest version of the CDR software is the best way to ensure that EDR Data Imaging Date Name) number retrieved data has been translated using the most current information provided by the manufacturers of the vehicles supported Crash Date Reported with CDR version Crash Data Retrieval Tool 21.3 by this product.

Filename BUICK 1G4GC5ER5DFXXXXXX_ACM.CDRX Reported with Software Licensed to (Company Collision Safety Institute Saved on Name) Imaged with CDR Crash Data Retrieval EDR Device Type version Airbag Control ModuleTool 17.9.1 CDR Information ImagedFile with Software Licensed to (Company Company Name information was removed when this file was saved without Event(s) recovered Deployment User Entered VIN 1G4GC5ER5DF****** Name) VIN sequence number User Reported with CDR version Crash Data Retrieval Tool 21.3 Case Number Reported with Software Licensed to (Company Comments Collision Safety Institute EDR Data Imaging Date Name) No comments entered.

Crash Date Type EDR Device Airbag Control Module Filenamerecovered BUICK 1G4GC5ER5DFXXXXXX_ACM.CDRX Event(s) Deployment Saved on Imaged with CDR version Crash Data Retrieval Tool 17.9.1 Data ImagedLimitations with Software Licensed to (Company Company Name information was removed when this file was saved without Comments Recorded Crash Events: Name) VIN sequence number No comments entered.

There are two recorded crash events for Front, Side, Rear (FSR) Events.

The first is the Non-Deployment Event. A Reported withtypes CDRofversion Crashand Data Retrieval Tool 21.3 Non-Deployment Event records datatobut does not deploy the air bag(s). The minimum SDM Recorded Vehicle Velocity Change, Reported with Software Licensed (Company Collision Safety Institute that is needed to record a Non-Deployment Event, is five MPH [8 km/h]. A Non-Deployment Event contains Pre-Crash and Crash Name) data. oldest Non-Deployment event can be overwritten by aControl Deployment Event, if all three records are full and the NonEDR The Device Type Airbag Module Deployment Event is not locked. A Non-Deployment Event can be overwritten by a more recent Non-Deployment Event if all three Data Limitations Event(s) recovered Deployment records areCrash full and the Non-Deployment is older than approximately 250 ignition cycles. Also, a Non-Deployment event can be Recorded Events: recorded onetypes of theoffollowing theFront, Deployment of any of(FSR) the frontal air bags, sideisair or roll bars: Event. A There areiftwo recordedoccurs crash without events for Side, and Rear Events.

The first thebags, Non-Deployment -Pretensioner(s) only Deployment Non-Deployment Event records data but does not deploy the air bag(s). The minimum SDM Recorded Vehicle Velocity Change, Comments -Head Rest Deployment that is needed to record a Non-Deployment Event, is five MPH [8 km/h]. A Non-Deployment Event contains Pre-Crash and Crash Cut-Off Deployment No comments entered. data.

The-Battery oldest Non-Deployment event can be overwritten by a Deployment Event, if all three records are full and the NonThe second type ofisSDM recordedAcrash event for FSR Events Deployment It also Non-Deployment contains Pre-Crash and ifCrash data.

Deployment Event not locked.

Non-Deployment Event canisbethe overwritten by aEvent. more recent Event all three Deployment Events cannot be overwritten cleared the SDM. records are full and the Non-Deployment isorolder thanby approximately 250 ignition cycles. Also, a Non-Deployment event can be Rollover Events contains Pre-Crash and Crash data.

Rollover event follow the same rules as FSR Deployment events. recorded if one of the following occurs without the Deployment of any of the frontal air bags, side air bags, or roll bars: The SDM can store up to three -Pretensioner(s) only Events.

Deployment Data Limitations -Head Rest Deployment Recorded Crash Events: Data: -Battery Cut-Off Deployment There areEvents, two types of Recorded recorded crash events for Side, and Rear (FSR) Events.

The the Non-Deployment For SDM Vehicle Velocity Change reflects theDeployment change in velocity that theiscontains sensing system experienced The FSR second type of SDM recorded crash event forFront, FSR Events is the Event.

It first also Pre-Crash andEvent.

Crash A data.

Non-Deployment Event records data but does not deploy the air bag(s). minimum Recorded Vehicle Velocity Change,time during the recorded portion of the event.

SDM Recorded Vehicle VelocityThe Change is theSDM change in velocity during the recording Deployment Events cannot be overwritten or cleared by the SDM. that is needed to record a Non-Deployment Event, is five MPH [8 km/h].

A Non-Deployment Event contains Pre-Crash and Crash and is notEvents the speed the vehicle wasand traveling event event and isfollow also not Barrier Velocity. For Deployment and Rollover contains Pre-Crash Crashbefore data. the Rollover thethe same rulesEquivalent as FSR Deployment events. data.

Thecan oldest Non-Deployment event can be by a Deployment Event, if all three records are also full and the up NonNon-Deployment Events, the SDM will record up overwritten to 300 milliseconds of data after time zero.

The SDM will record to 300 The SDM store up to three Events.

Deployment is notAcceleration locked. A Non-Deployment Event can be overwritten by a more recent Non-Deployment Event if all three milliseconds Event of Vehicle data after time zero. records are full and the older than approximately 250Acceleration, ignition cycles. a Non-Deployment event can be For Rollover Events, theNon-Deployment SDM may recordisLateral Acceleration, Vertical andAlso, Roll Rate data, if the SDM is rollover Data: recorded ifThis onedata ofSDM the following without the Change Deployment of any the air bags, air bags,system orFor rollRollover bars: capable. reflects whatoccurs the sensing system experienced during thefrontal recorded portion of the event.

Deployment For FSR Events, Recorded Vehicle Velocity reflects theof change in velocity thatside the sensing experienced -Pretensioner(s) Deployment Events,the the SDM willportion recordonly upthe to 700 milliseconds of dataVehicle before Velocity the Deployment is met and 290 milliseconds after the time during recorded of event.

SDM Recorded Change criteria is the change in velocity during the recording -Head Rest Deployment Deployment criteria is met. and is not the speed the vehicle was traveling before the event and is also not the Barrier Equivalent Velocity. For Deployment and -Battery Cut-Off Deployment -Deployment loops may bethe displayed beingup deployed in a Non-Deployment event record, a Deployment event is qualified during Non-Deployment Events, SDM willasrecord to 300 milliseconds of data after time zero. ifThe SDM will also record up to 300 The second type of SDM recorded crash for FSR is the Deployment Event.

It also contains Pre-Crash andevent Crashand data. the Non-Deployment event.

That is, if twoevent or more events are occurring at the same time and one is a Non-Deployment milliseconds of Vehicle Acceleration data after time zero.Events Deployment Events cannot be overwritten or cleared by the SDM. one Rollover of the others is athe Deployment the Deployment is qualified while the stillSDM active, the For Events, SDM mayevent, recordand Lateral Acceleration,event Vertical Acceleration, andNon-Deployment Roll Rate data, ifisthe is rollover Rollover Eventsmay contains Pre-Crash andNon-Deployment Crash data. Rollover follow the same rules as FSR Deployment events. deployed be recorded in the event event record. capable. loops This data reflects what the sensing system experienced during the recorded portion of the event. For Rollover Deployment The SDM can store up is torecorded three Events.

-Time between events in 10 msec intervals andbefore is displayed in secondscriteria for a maximum time 655.33 seconds.

The Events, the SDM will record up to 700 milliseconds of data the Deployment is met and 290ofmilliseconds after the counter measures time from the start of one event to the start of the next event if both events occur within the same ignition Deployment criteriathe is met.

Data: cycle.

-Deployment loops may be displayed as being deployed in a Non-Deployment event record, if a Deployment event is qualified during For FSR Events,SDM SDMevent.

Recorded Vehicle Velocity Change reflects the change in velocity system -The Maximum Recorded Vehicle Velocity Change occur between recorded 10 the millisecond sample experienced points event of the and SDM the Non-Deployment That is, if two or more eventsmay are occurring at thethe same timethat and onesensing is a Non-Deployment during recorded of the event.

SDMthe Recorded Vehicle Velocity Change is the in velocityisduring the recording time Recorded Change. one of the the Vehicle others isVelocity aportion Deployment event, and Deployment event is qualified while the change Non-Deployment still active, the and is not the speed the vehicle was traveling before the event and is also not the Barrier Equivalent Velocity.

For Deployment and -Event Recording Complete will indicate if data from the recorded event has been fully written to the SDM memory or if it has been deployed loops may be recorded in the Non-Deployment event record.

Non-Deployment Events, the SDM will record up to 300 milliseconds of data after time zero.

The SDM will also record up to 300 interrupted and events not fullyiswritten.

-Time between recorded in 10 msec intervals and is displayed in seconds for a maximum time of 655.33 seconds. The milliseconds of Vehicle Acceleration data of after time zero.

-SDM Vehicle Speed can be affected by start various factors, but events not limited to within the following: counterRecorded measures the time from accuracy the start one event to the of the next including event if both occur the same ignition For Rollover Events, the SDM may record Lateral Acceleration, Vertical Acceleration, and Roll Rate data, if the SDM is rollover 1G4GC5ER5DF****** Page 1 of 23 Printed on: Sunday, October 31 2021 at 09:42:49 cycle.

-Significant changes in the tire’s rolling radius capable.

This data reflects what the sensing system experienced during the recorded portion of the event.

For Rollover Deployment -The Maximum Vehicle Velocity Change may occur between the recorded 10 millisecond sample points of the SDM -Final SDM drive Recorded axle ratio changes Events, theVehicle SDM will recordChange. up to 700 milliseconds of data before the Deployment criteria is met and 290 milliseconds after the Recorded Velocity -Wheel lockup and wheel slip Deployment criteria is met.

-EventSwitch Recording Complete will indicate data from thestate recorded beencircuit. fully written to the SDM memory or if it has been -Brake Circuit Status indicates theifbeing open/closed theevent brakehas switch -Deployment loops may be displayed as deployed in a of Non-Deployment event record, if a Deployment event is qualified during interrupted and not fully written.

-Pre-Crash data is recorded asynchronously.

The 0.5 second Pre-crashatdata value recent data point) isevent the data the Non-Deployment event.

That is, if twocan or more events arevarious occurring theincluding same (most time one recorded is a Non-Deployment and -SDM Recorded Vehicle Speed accuracy be affected by factors, butand not limited the following: point last sampled Time Zero.

That is to the last data point may have been justtobefore Time Zero the but no more one of the others isbefore a Deployment event, and thesay, Deployment event is qualified while thecaptured Non-Deployment is still active, 1G4GC5ER5DF****** Page 1 values of 23 are referenced from Printedthis on: data Sunday, October 31 2021 at 09:42:49 than 0.5 second before Time Zero.

All subsequent Pre-crash data point. deployed loops may be recorded in the Non-Deployment event record.

-Pre-Crash Electronic Validity in Check Status indicates Invalid”inif:seconds for a maximum time of 655.33 seconds. The -Time between eventsData is recorded 10 msec intervals and “Data is displayed -The SDM receives a message with anevent “invalid” flag from sending pre-crash counter measures the time from the start of one to the start of the the module next event if boththe events occur data within the same ignition -Pre-Crash Electronic Data Validity Check Status indicates “Data Not Available” if: cycle.

-No data is received from the module sending the pre-crash data -The Maximum SDM Recorded Vehicle Velocity Change may occur between the recorded 10 millisecond sample points of the SDM -For diesel Vehicle poweredVelocity vehicles, the data displayed as Throttle Position (%) is actually the data for the Air Inlet Flap Position. This is Recorded Change. not the same as theComplete throttle position for aifgasoline powered engines.

-Event Recording will indicate data from the recorded event has been fully written to the SDM memory or if it has been -Belt Switch and Circuit indicates the status of the seat belt switch circuit. interrupted notStatus fully written.

-The ignition cycleVehicle counterSpeed will increment the power by mode cycles from OFF/Accessory RUN.toApplying and removing of -SDM Recorded accuracywhen can be affected various factors, including but notto limited the following: battery power to the module will not increment the ignition cycle counter.

1G4GC5ER5DF****** Page 1 of 23 Printed on: Sunday, October 31 2021 at 09:42:49 -Ignition Cycles Since DTCs Were Last Cleared can record a maximum value of 253 cycles and can only be reset by a scan tool.

-Dynamic Deployment Event Counter tracks the number of Deployment events that have occurred during the SDM’s lifetime.

-Dynamic Event Counter tracks the number of qualified events (either Deployments, Non-deploy, or Rollover events) that have occurred during the SDM’s lifetime.

-For Deployment Events, DTC B0052 (Deployment commanded) shall be recorded with the remainder of the data for this event even though it occurred after Event Enable.

-Once a firing loop has been commanded to be deployed, it will not be commanded to be deployed again during the same ignition cycle. Firing loop times for subsequent deployment type events, during the same ignition cycle, will record the deployment times as N/A. -A Concurrent Event is when two events are happening nearly simultaneously. The “Concurrent Event Flag Set” parameter will indicate “Yes” if one event begins, but before that event is qualified, another event begins and is qualified.

A Non-Deployment event typically becomes qualified if that event exceeds the 5 MPH (8 km/h) delta V recording threshold and the event has concluded. A deployment event (FSR or Rollover) becomes qualified when a deployment has been commanded for that event.

Example of a Concurrent Event: A Rollover event begins. Before the Rollover event is qualified, a Non-Deployment event begins and is qualified. Sometime after the Non-Deployment event is qualified, the Rollover event is qualified. The Non-Deployment event will be recorded in the first open record even though the Rollover event enabled before the Non-Deployment event. The Rollover event will be recorded in the next open record. The “Concurrent Event Flag Set” parameter will indicate “Yes” for the Rollover event. The “Time Between Events” parameter will indicate the time from the start of the Rollover event to the start of the Non-Deployment event.

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-Dynamic Event Counter tracks the number of qualified events (either Deployments, Non-deploy, or Rollover events) that have occurred during the SDM’s lifetime.

-For Deployment Events, DTC B0052 (Deployment commanded) shall be recorded with the remainder of the data for this event even though it occurred after Event Enable.

-Once a firing loop has been commanded to be deployed, it will not be commanded to be deployed again during the same ignition cycle. Firing loop times for subsequent deployment type events, during the same ignition cycle, will record the deployment times as N/A. -A Concurrent Event is when two events are happening nearly simultaneously. The “Concurrent Event Flag Set” parameter will indicate “Yes” if one event begins, but before that event is qualified, another event begins and is qualified.

A Non-Deployment event typically becomes qualified if that event exceeds the 5 MPH (8 km/h) delta V recording threshold and the event has concluded. A deployment event (FSR or Rollover) becomes qualified when a deployment has been commanded for that event.

Example of a Concurrent Event: A Rollover event begins. Before the Rollover event is qualified, a Non-Deployment event begins and is qualified. Sometime after the Non-Deployment event is qualified, the Rollover event is qualified. The Non-Deployment event will be recorded in the first open record even though the Rollover event enabled before the Non-Deployment event. The Rollover event will be recorded in the next open record. The “Concurrent Event Flag Set” parameter will indicate “Yes” for the Rollover event. The “Time Between Events” parameter will indicate the time from the start of the Rollover event to the start of the Non-Deployment event.

-The GM parameter name is displayed in parentheses after the NHTSA Part 563 parameter name.

-The reported range of the longitudinal and lateral acceleration values is approximately ± 50 g.

-All data should be examined in conjunction with other available physical evidence from the vehicle and scene.

Data Source: All SDM recorded data is measured, calculated, and stored internally, except for the following: -Vehicle Status Data (Pre-Crash) is transmitted by the Body Control Module, via the vehicle’s communication network.

-The Belt Switch Circuit is wired directly to the SDM. 1G4GC5ER5DF****** Page 2 of 23 Printed on: Sunday, October 31 2021 at 09:42:49 Data Element Sign Convention: The following table provides an explanation of the sign notation for data elements that may be included in this CDR report.

Directional references to sign notation are all from the perspective of the driver when seated in the vehicle facing the direction of forward vehicle travel.

Data Element Name Longitudinal Acceleration Longitudinal Velocity Change Lateral Acceleration Lateral Velocity Change Vertical Acceleration Roll Rate

Positive Sign Notation Indicates Forward Forward Left to Right Left to Right Downward Clockwise Rotation

Hexadecimal Data: Data that the vehicle manufacturer has specified for data retrieval is shown in the hexadecimal data section of the CDR report. The hexadecimal data section of the CDR report may contain data that is not translated by the CDR program. The control module contains additional data that is not retrievable by the CDR tool.

01043_SDM10P-conti_r017

System Status at Time of Retrieval

Dynamic Deployment Event Counter Multi-Event, Number of Events (Dynamic Event Counter) Dynamic OnStar Notification Event Counter Vehicle Identification Number (VIN) Ignition Cycle, Download (Ignition Cycles at Investigation) End Model Part Number System Type Software Module Identifier 1 Software Module Identifier 2 Software Module Identifier 3 Manufacturing Traceability Data, Component Identifier Manufacturing Traceability Data, Part Number/Broadcast Code Manufacturing Traceability Data, Supplier Code Manufacturing Traceability Data, Traceability Number ESS # 1 Traceability Data, Component Identifier ESS # 1 Traceability Data, Part Number/Broadcast Code ESS # 1 Traceability Data, Supplier Code ESS # 1 Traceability Data, Traceability Number ESS # 2 Traceability Data, Component Identifier ESS # 2 Traceability Data, Part Number/Broadcast Code ESS # 2 Traceability Data, Supplier Code ESS # 2 Traceability Data, Traceability Number ESS # 3 Traceability Data, Component Identifier ESS # 3 Traceability Data, Part Number/Broadcast Code ESS # 3 Traceability Data, Supplier Code ESS # 3 Traceability Data, Traceability Number ESS # 4 Traceability Data, Component Identifier ESS # 4 Traceability Data, Part Number/Broadcast Code ESS # 4 Traceability Data, Supplier Code ESS # 4 Traceability Data, Traceability Number ESS # 5 Traceability Data, Component Identifier ESS # 5 Traceability Data, Part Number/Broadcast Code ESS # 5 Traceability Data, Supplier Code 22 Collision MagazineESS - Volume 15 Issue 2 Data, Traceability Number www.collisionmagazine.com # 5 Traceability ESS # 6 Traceability Data, Component Identifier ESS # 6 Traceability Data, Part Number/Broadcast Code ESS # 6 Traceability Data, Supplier Code ESS # 6 Traceability Data, Traceability Number ESS # 7 Traceability Data, Component Identifier Volume 15 Issue 2.indd 22 ESS # 7 Traceability Data, Part Number/Broadcast CodePage 3 of 23 1G4GC5ER5DF******

1 1 1 1G4GC5ER5DF****** 7822 00CF5685 Continental 00CF5682 015B7B4A 00CF2D8A AS 8101 T 122530034 AU 2341 T 7LI364IGN AT 2341 T NHGM9SIGN AH 2340 T 8S2PPKEGL AJ 2340 T WS2VFKEGL 00 0000 T 000000000 00 0000 T 000000000 00 Printed on: Sunday, October 310000 2021 at 09:42:49

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System Status at Event (Event Record 1)

Event Record Type OnStar Deployment Status Data Sent Complete file recorded (Event Recording Complete) Crash Record Locked OnStar SDM Recorded Vehicle Velocity Change Data Sent Deployment Event Counter Multi-Event, Number of Events (Event Counter) OnStar Notification Event Counter Time From Event 1 to 2 (Time Between Events) (seconds) Ignition Cycle, Crash (Ignition Cycles at Event) Algorithm Active: Frontal Algorithm Active: Side Algorithm Active: Rollover Algorithm Active: Rear Concurrent Event Flag Set Event Severity Status: Frontal Pretensioner Event Severity Status: Frontal Stage 1 Event Severity Status: Frontal Stage 2 Event Severity Status: Left Side Event Severity Status: Right Side Event Severity Status: Rear Event Severity Status: Rollover Safety Belt Status, Driver (Driver Belt Switch Circuit Status) Safety Belt Status, Right Front Passenger (Passenger Belt Switch Circuit Status) Center Front Row Belt Switch Circuit Status (If Equipped) Left Row 3 Belt Switch Circuit Status (If Equipped) Center Row 3 Belt Switch Circuit Status (If Equipped) Right Row 3 Belt Switch Circuit Status (If Equipped) Passenger Seat Occupancy Status Occupant Size Right Front Passenger Child (Passenger Classification Status) Passenger Air Bag ON Indicator Status Passenger Air Bag OFF Indicator Status Low Tire Pressure Warning Lamp Status 0.5 Seconds Prior to Time Zero Frontal Air Bag Warning Lamp (SIR Warning Lamp Status 0.5 Seconds Prior to Time Zero) SIR Warning Lamp ON/OFF Time Continuously (seconds) Number of Ignition Cycles SIR Warning Lamp was ON/OFF Continuously Ignition Cycles Since DTCs Were Last Cleared 0.5 Seconds Prior to Time Zero Maximum Delta-V, Longitudinal (Maximum Longitudinal SDM Recorded Vehicle Velocity Change for FSR Event) MPH [km/h] Time, Maximum Delta-V (Time From FSR Time Zero to Maximum Longitudinal SDM Recorded Vehicle Velocity Change)(msec) Maximum Delta-V, Lateral (Maximum Lateral SDM Recorded Vehicle Velocity Change for FSR Event) MPH [km/h] Time Maximum Delta-V, Lateral (Time From FSR Time Zero to Maximum Lateral SDM Recorded Vehicle Velocity Change)(msec)

Deployment Yes Yes Yes No 1 1 1 Data Not Available 7822 Yes Yes No Yes No Yes Yes Yes No No No No Not Buckled Not Buckled Data Not Available Data Not Available Data Not Available Data Not Available Empty No (Not Applicable) Off On Off Off 655330 7822 142 -48 [-77] 138 -2 [-3] 88

Event Data (Event Record 1)

Driver 1st Stage Deployment Loop Commanded Yes Passenger 1st Stage Deployment Loop Commanded No Driver 2nd Stage Deployment Loop Commanded Yes Passenger 2nd Stage Deployment Loop Commanded No Driver Pretensioner Deployment Loop #1 Commanded Yes Passenger Pretensioner Deployment Loop #1 Commanded Yes Driver Pretensioner Deployment Loop #2 Commanded Yes Passenger Pretensioner Deployment Loop #2 Commanded Yes Driver Thorax Loop Commanded No Passenger Thorax Loop Commanded No Left Row 1 Roof Rail/Head Curtain Loop Commanded Yes Right Row 1 Roof Rail/Head Curtain Loop Commanded Yes Frontal Air Bag Deployment, Time to 1st Stage Deployment, Driver (Driver 1st Stage 3 Time From Time Zero to Deployment Command Criteria Met) (msec) Frontal Air Bag Deployment, Time to 2nd Stage, Driver (Driver 2nd Stage Time From 8 Time Zero to Deployment Command Criteria Met) (msec) Frontal Air Bag Deployment, Time to 1st Stage Deployment, Right Front Passenger Printed on: Sunday, October 31 2021 at 09:42:49 1G4GC5ER5DF****** Page 5 of 23 (Passenger 1st Stage Time From Time Zero to Deployment Command Criteria Met) Data Not Available (msec) Frontal Air Bag Deployment, Time to 2nd Stage, Right Front Passenger (Passenger Data Not Available 2nd Stage Time From Time Zero to Deployment Command Criteria Met) (msec) Side air bag deployment, time to deploy, driver (Driver Thorax/Curtain Time From 4 Time Zero to Deployment Command Criteria Met) (msec) Side air bag deployment, time to deploy, right front passenger (Passenger Thorax/Curtain Time From Time Zero to Deployment Command Criteria Met) (msec) Pretensioner Deployment, Time to Fire, Driver (Driver Pretensioner Time From Time Zero to Deployment Loop #1 or Loop #2 Command Criteria Met) (msec) Pretensioner Deployment, Time to Fire, Right Front Passenger (Passenger Pretensioner Time From Time Zero to Deployment Loop #1 or Loop #2 Command Criteria Met) (msec)

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Longitudinal Crash Pulse (Event Record 1)

Lateral Crash Pulse (Event Record 1)

Delta-V, Longitudinal (SDM Recorded Vehicle Longitudinal Velocity Change for FSR Event)

80

Delta-V, Lateral (SDM Recorded Vehicle Lateral Velocity Change for FSR Event)

40 120

70

60

110 100

60

90

50

50

30

80

40

70

40

20

60

30

50

30

40 20

20

10

10

-10

-10

0

0

-20

-10

-30

-20

-10

-40 -30

-20

-50 -60

-40

-30

-20

-70 -80

-50

-40

-90

-60

-30

-100

-50

-110

-70

-120 -80

0

20

40

60

80

100

140

msec

160

180

200

220

240

260

280

-60 -40

300

0

20

40

60

80

100

50

50

40

40

30

30

20

20

10

10

0

140

msec

160

180

200

220

240

260

280

200

220

240

260

280

300

0

-10

-10

-20

-20

-30

-30

-40

-40

-50

120

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event)

60

g

g

120

Longitudinal Acceleration (SDM Recorded Vehicle Longitudinal Acceleration for FSR Event)

60

-60

k m /h

0

MPH

10

0

k m /h

MPH

20 10

30

-50

0

20

40

60

80

100

1G4GC5ER5DF******

120

140

msec

160

180

200

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220

240

260

280

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Time (msec)

Delta-V, Longitudinal (SDM Recorded Vehicle Longitudinal Velocity Change for FSR Event) (MPH)

Delta-V, Longitudinal (SDM Recorded Vehicle Longitudinal Velocity Change for FSR Event) (km/h)

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300

-3.7 -9.3 -16.2 -21.1 -30.4 -39.8 -42.3 -43.5 -44.7 -46.0 -46.6 -47.2 -47.2 -47.8 -47.2 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6 -46.6

-6.0 -15.0 -26.0 -34.0 -49.0 -64.0 -68.0 -70.0 -72.0 -74.0 -75.0 -76.0 -76.0 -77.0 -76.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0 -75.0

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0

20

40

60

80

100

120

140

msec

1G4GC5ER5DF******

160

180

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300

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Lateral Crash Pulse (Event Record 1)

Longitudinal Crash Pulse (Event Record 1)

24 Collision Magazine - Volume 15 Issue 2

-60

300

Time (msec)

Delta-V, Lateral (SDM Recorded Vehicle Lateral Velocity Change for FSR Event) (MPH)

Delta-V, Lateral (SDM Recorded Vehicle Lateral Velocity Change for FSR Event) (km/h)

10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300

0.6 0.0 0.0 0.0 -1.2 -0.6 -1.2 -0.6 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.2 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9 -1.9

1.0 0.0 0.0 0.0 -2.0 -1.0 -2.0 -1.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -2.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0 -3.0

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Longitudinal Crash Pulse (Event Record 1) Time (msec)

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100

Longitudinal Acceleration (SDM Recorded Vehicle Longitudinal Acceleration for FSR Event) (g) -19.0 -8.2 -24.2 -8.2 -23.0 -19.0 -29.0 -18.2 -22.2 -32.2 -12.2 -28.2 -42.2 -34.2 -37.0 -31.0 -4.2 -20.2 -18.2 -38.2 -44.2 -43.0 -39.0 -31.0 -44.2 -44.2 -34.2 -44.2 -44.2 -43.0 -42.2 -17.0 -0.2 1.8 -0.2 -0.2 -1.0 -13.0 -7.0 -8.2 -0.2 -11.0 -4.2 -3.0 -2.2 -3.0 -7.0 -8.2 -7.0 -5.0

Time (msec)

102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200

Longitudinal Time Longitudinal Acceleration (msec) Acceleration (SDM Recorded Vehicle (SDM Recorded Vehicle Longitudinal Longitudinal Acceleration for FSR Acceleration for FSR Event) (g) Event) (g) -5.0 202 -0.2 -6.2 204 -0.2 -3.0 206 -0.2 -2.2 208 -0.2 1.8 210 -0.2 1.0 212 -0.2 1.0 214 -0.2 -5.0 216 -0.2 -3.0 218 -0.2 -3.0 220 -0.2 -1.0 222 -0.2 1.0 224 -0.2 -0.2 226 -0.2 -0.2 228 -0.2 -4.2 230 -0.2 -2.2 232 -0.2 -0.2 234 -0.2 -0.2 236 -0.2 -2.2 238 -0.2 -1.0 240 -0.2 1.8 242 -0.2 3.0 244 -0.2 3.8 246 -0.2 1.0 248 -0.2 1.0 250 -0.2 Contains Data -0.2 No Recorded 252 -0.2 3.0 254 -0.2 3.0 256 -0.2 3.0 258 -0.2 1.0 260 -0.2 -0.2 262 -0.2 1.0 264 -0.2 1.8 266 -0.2 -0.2 268 -0.2 -0.2 270 -0.2 -0.2 272 -0.2 -0.2 274 -0.2 -0.2 276 -0.2 -0.2 278 -0.2 -0.2 280 -0.2 -0.2 282 -0.2 -0.2 284 -0.2 -0.2 286 -0.2 -1.0 288 -0.2 -0.2 290 -0.2 -0.2 292 -0.2 -0.2 294 -0.2 1.0 296 -0.2 1.0 298 -0.2 -0.2 300 -0.2

1G4GC5ER5DF******

Rollover Crash Pulse (Event Record 1) SDM Recorded Vehicle Roll Rate

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Rollover Crash Pulse (Event Record 1) SDM Recorded Vehicle Roll Rate

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Rollover Crash Pulse (Event Record 1) Lateral Acceleration (SDM Recorded Vehicle Lateral Acc Event)

Contains No Recorded Data Contains No Recorded Data

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Lateral Crash Pulse (Event Record 1) Time (msec)

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event) (g)

Time (msec)

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event) (g)

Time (msec)

Lateral Acceleration (SDM Recorded Vehicle Lateral Acceleration for FSR Event) (g)

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 100

1.0 -1.0 3.0 17.8 -3.0 -15.0 1.0 -20.2 15.0 7.8 -8.2 9.0 -2.2 -7.0 5.8 -6.2 3.8 -5.0 -10.2 -1.0 -0.2 -0.2 -4.2 -12.2 -0.2 -0.2 9.0 -0.2 -0.2 -0.2 -1.0 -7.0 7.0 -5.0 -5.0 -0.2 1.8 -0.2 -0.2 3.0 -3.0 -5.0 -4.2 -1.0 -2.2 -1.0 -0.2 3.8 -4.2 -0.2

102 104 106 108 110 112 114 116 118 120 122 124 126 128 130 132 134 136 138 140 142 144 146 148 150 152 154 156 158 160 162 164 166 168 170 172 174 176 178 180 182 184 186 188 190 192 194 196 198 200

3.0 -2.2 -1.0 -1.0 -1.0 1.8 1.0 -3.0 1.0 -0.2 -0.2 -3.0 -1.0 1.8 1.8 -0.2 -1.0 -0.2 1.8 1.0 -0.2 1.8 -0.2 -0.2 1.0 -1.0 -1.0 -1.0 -0.2 -0.2 -0.2 -1.0 -0.2 -0.2 1.0 -1.0 -0.2 -0.2 -0.2 -1.0 -0.2 -0.2 -0.2 -1.0 -0.2 -0.2 -1.0 -0.2 -0.2 -1.0

202 204 206 208 210 212 214 216 218 220 222 224 226 228 230 232 234 236 238 240 242 244 246 248 250 252 254 256 258 260 262 264 266 268 270 272 274 276 278 280 282 284 286 288 290 292 294 296 298 300

-0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.2

Vertical Crash Pulse (Event Record 1) Normal Acceleration (SDM Recorded Vehicle Vertical Acceleration for Rollover Event) 1G4GC5ER5DF****** Contains No Recorded Data

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Pre-Crash Data -5.0 to -0.5 sec (Event Record 1) Times (sec) -5.0 -4.5 -4.0 -3.5 -3.0 -2.5 -2.0 -1.5 -1.0 -0.5

Accelerator Pedal, % Full (Accelerator Pedal Position) 99 99 99 99 99 99 99 99 99 49

Service Brake (Brake Switch Circuit State)

Engine RPM (Engine Speed)

Engine Throttle, % Full (Throttle Position)

Speed, Vehicle Indicated (Vehicle Speed) (MPH [km/h])

Off Off Off Off Off Off Off Off Off Off

5376 5376 5376 5376 5440 5440 5440 5504 5504 5504

81 83 85 83 84 83 80 80 80 82

106 [ 171] 107 [ 172] 107 [ 172] 107 [ 173] 107 [ 173] 108 [ 174] 108 [ 174] 109 [ 175] 109 [ 176] 109 [ 176]

Pre-Crash Data -2.0 to -0.5 sec (Event Record 1) Times (sec)

Cruise Control Active

Cruise Control Resume Switch Active

Cruise Control Set Switch Active

Engine Torque (lb-ft [N-m])

Reduced Engine Power Mode Indicator

-2.0 -1.5 -1.0 -0.5

No No No No

No No No No

No No No No

163 [ 221] 162 [ 220] 162 [ 219] 161 [ 218]

Off Off Off Off

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Using Civil Twilight Envelopes to Describe Commercial Trucks’ Headlamp Illumination Jeffrey Andre, PhD Department of Psychology, James Madison University, Harrisonburg, VA Contrast Associates, LLC, Moseley, VA

April Yergin, BS Allen Vaughan, BS ATA Associates, Inc., Houston, TX

A

bstract The present study quantified headlamp illumination from commercial trucks using the civil twilight envelope methodology (Andre & Owens, 2001). This approach uses visual performance data during civil twilight’s illumination levels to provide an easily understood description of useful headlamp illumination.

Our goal was to provide truck drivers with information concerning the usefulness of their headlamps. We used the dark limit of civil twilight as our demarcation of farthest distance receiving useful illumination. Illumination measurements from four new commercial trucks were collected at night using a procedure similar to Andre and Owens. We found civil twilight distances similar to those found previously in passenger vehicles. Along the vehicle’s midline, the average civil twilight distance was 221.4 ft with low beams and 571.9 ft with high beams. There was also a substantial amount of variability among measurement locations for both beam intensities. Pursuant to our study’s goal, we report information that could educate drivers about their headlamp systems’ capabilities and limitations as defined by human visual performance in low light conditions.

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Introduction While the human visual system functions over a wide range of illumination, light levels present during civil twilight define a vital transition time when visual capabilities to identify unmarked, unlit objects become severely degraded. In 2001, Andre and Owens described vehicle headlamp illumination in terms of a “civil twilight envelope” to estimate the extent of useful illumination in front of a vehicle. Given that there are approximately 2.9 million tractor-trailers on United States roads (US Department of Transportation; 2017 data), the present study measured the headlamp illumination of commercial tractor-trailers using the civil twilight envelope methodology in an attempt to quantify the extent of useful illumination in front of such vehicles. Importantly, the goal of our study was not to compare or differentiate among truck manufacturers, but rather to provide a market-weighted analysis of the current fleet.

This paper will first introduce existing headlamp regulations. Next, we will briefly explain the two-visual systems hypothesis before discussing what civil twilight is and why its illumination levels are important to understanding human visual performance. Finally, a review of the Andre and Owens (2001) research will precede a discussion of the current study's rationale, experimental methodology, findings, and discussion.

of their vehicle’s lighting systems when driving at night. Indeed, the regulations discuss neither headlamp throw distance nor relevance to drivers’ visual performance. Headlamp lighting information should be provided in an easily understood format based on the human visual system and described in common roadway parameters.

The Two Visual Systems The human visual system has evolved for both bright and dark conditions, although our “night” vision is not nearly as capable. During the daytime, visual performance is determined mostly by cone photoreceptors’ functioning in and around the eye’s fovea located in the central retina. The cones help us see fine details (visual acuity), varying brightness levels (contrast sensitivity), an object’s colors and movement. Cones are not as sensitive to light as their counterpart rod photoreceptors, because they function primarily in bright conditions when there is an abundance of light to capture. Rods, conversely, are extremely sensitive to light since they mostly operate in low light or nighttime conditions. Rods detect brightness differences but do not “see” color, nor is their acuity typically adequate for object identification.

The day-night distinction between photoreceptors is one physiological basis for the two-visual systems hypothesis. The theory’s premise is the physiological existence of sepaExisting Headlamp Regulations rate cortical streams of processing of “what” an object is versus “where” it is (or “how” to The lengthy Code of Federal Regulations interact with it). Some of the earliest work (§571.108) that discusses headlamps also in- on this can be traced back to the late 1960s cludes information on other reflective devices when a quartet of researchers published their found on both the front and rear of vehicles research on mammals and fish that suggested including commercial tractor trailers, school the dichotomy (see Held, 1968; Ingle, 1967; busses, and motorcycles. The headlamp sec- Schneider, 1967; Trevarthen, 1968). Trevartions of the code discuss headlamp types, then later named the “what” pathway focal vimounting positions, aiming procedures/ sion, and it consists of our ability to discrimimechanisms, replacement bulb information, nate patterns, colors, and object recognition and beam photometric requirements. These (see, e.g., Owens, 2003). It uses mainly the beam requirements are discussed in terms of central retina and fovea. minimum and maximum photometric intensity values (in candelas) which are broken Conversely, the “where” or ambient pathway down by different test point locations (in de- consists of postural control, orientation, and grees). Unfortunately, these codes are practi- visual guidance activities and utilizes more of cally useless to drivers who are interested in the peripheral retina. Factors that differenunderstanding the capabilities and limitations tiate the two systems include low light (diswww.collisionmagazine.com

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cussed in 1.3 below), refractive error (blur), visual field restriction, and prismatic optical rearrangement (see e.g., Andre & Rogers, 2005).

on guidance vision, which still operates quite effectively at night. Of course, tasks that require visual recognition are also important at night. But thanks to good engineering, these focal abilities are partially enhanced by lighting and reflectorization. Consequently, drivers are not likely to recognize that their ability to see dim, low-contrast objects is drastically degraded in the night road environment. So, they tend to be overconfident at night, driving as though they can see as well as in daylight (Leibowitz & Owens, 1977, 1991; Leibowitz, Owens & Post, 1982).”

Humans use both of these modes of processing when driving. Focal driving tasks include such behaviors as recognizing other vehicles, identifying roadway hazards, and reading instruments and traffic signs. Ambient driving tasks include maintaining lane position, travel direction, and vehicle speed (Owens & Andre, 1996).

Civil Twilight and Its Impact on Vision Three different twilights separate “daytime” from “nighttime.” They occur directly before sunrise and after sunset. The first after sunset is civil twilight when the sun is tangent with the horizon to when it is 6° degrees below the horizon. The next is nautical twilight when the sun is between 6° and 12° below the horizon, and the third is astronomical twilight and is when the sun is 12° to 18° below the horizon. Twilight order is reversed before sunrise, with astronomical occurring first. In the middle latitudes of the United States, each of these twilights lasts for approximately 30 minutes, although exact lengths depend on one’s specific location and time of year.

Historically, outdoor activities prevalent in the early part of the 20th century were considered safe during illumination levels found during civil twilight, approximately 330 lux at sunset to a “dark limit” of 3.3 lux. For example, many states still allow hunting from 30 minutes before sunrise to 30 minutes after sunset. This assertion was challenged by Leibowitz and Owens (1991), who argued that with technological advances, e.g., automobiles, have come complications to visual processing: During the transition towards the dark limit of civil twilight, large deficits in focal visual functioning become evident, while ambient vision remains relatively unaffected (see, e.g., Owens, Francis & Leibowitz, 1989; Owens & Tyrrell, 1999). Leibowitz and Owens (1977) originally termed this the selective degradation of the focal system, because ambient processing is relatively unaffected.

The problem with selective degradation for night drivers is perhaps best summarized by Owens (2003), who stated: (italics added) “On the basis of the selective degradation hypothesis, we proposed that, for the most part, night drivers are simply not aware that their vision is impaired. We reasoned that driving, like many complex tasks, involves both the recognition and the guidance modes of vision. Because steering is of constant importance, driving success and confidence probably depend heavily

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Civil Twilight Distance By the dark limit of civil twilight, when the ambient illumination is 3.3 lux, visual detection and identification of objects are significantly impaired. Could this fact be used to help explain the limits of a vehicle's headlamp illumination that night drivers use to see objects and hazards on the road? To answer this, Andre and Owens (2001) measured the distance in front of a vehicle where its headlamp illumination fell to 3.3 lux. This "civil twilight distance" would define the farthest extent of potentially useful headlamp illumination. Using a functional approach to describing headlamp illumination (Owens, Francis & Leibowitz, 1989), they quantified headlamp illumination for 13 passenger cars along seven axes representing a twolane roadway's dimensions. When combined, the distances along these seven axes defined an "envelope" of useful illumination in front of the vehicle.

Andre and Owens (2001) stated their aggregate recorded civil twilight distances (CTDs) were consistent with market headlamp data provided by Sivak, Flannagan, Kojima, and Traube (1997). In addition, Andre and Owens reported: (Note: Unless otherwise stated, data reported below are from the center of the travel lane along the vehicle’s midline.)

•

Ground-level, low beam CTD averaged approximately 230 ft.

•

CTDs were reduced in the oncoming lane.

•

CTDs were severely attenuated when measured at an elevated position above the road surface. Low beam CTD was approximately 26% of ground-level distance (60 ft. vs. 230 ft).

•

Ground-level, high beam CTD averaged approximately 570 ft.

•

The CTD attenuation at the elevated measurement location was not as severe with high beams. The el-

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•

evated position CDT was approximately 88% of the ground level distance (500 ft vs. 570 ft).

mately 75% of the truck market (as of December 2019; source: statista.com).

There was a great amount of variability among vehicles tested: At ground level, the shortest distance was only 56% of the farthest for low beams (175 ft) and less than 38% for high beams (over 470 ft).

We use a calibrated Extech Instruments Heavy Duty Series Light Meter, Model HD450, for all illumination measurements, and a Spectra Precision Focus 35 Series robotic total station for verifying location and distance information.

(Note: Unless otherwise stated, data reported below are from the center of the travel lane along the vehicle’s midline.)

It is important to note that CTD estimates the extent of potentially useful illumination produced from vehicle headlamps. It was developed from data on how the human visual system functions in low illumination environments.

CTD is not a “detection” distance like those provided by point-prediction models, which have been called into question regarding false precision (see, e.g., Francis, Tyrrell & Owens, 2020; Owens, 1999). A detection distance is based on many factors related to the object (e.g., contrast, size, color, movement), environment (e.g., existing lighting, time of day, precipitation), and observer (e.g., fixation location, attention, expectation, age).

The Current Study Given the importance for truck drivers to understand their vehicle’s headlamp illumination, the present study sought to measure headlamp beam patterns from multiple commercial vehicles using the civil twilight envelope approach.

Notably, our study aimed to obtain aggregate CTDs and not differentiate between trucks’ brands. This information could be used to educate drivers about their headlamp systems’ capabilities and limitations in a way that is relevant to what they can see when driving at night.

Experimental Methodology We measured headlamp illumination from four new Class 8 conventional style tractors on March 12, 2020. We completed all of our testing at the Houston Community College North East Campus in Houston, TX. Equipment In our study, we tested the following 2020 trucks (listed in alphabetical order): (1) Freightliner tractor, odometer reading = 12 miles, LED low and high beam headlamps, LED marker lamps; (2) International tractor, odometer = 555 miles, LED low beams, incandescent high beams, LED marker lamps; (3) Peterbilt tractor, odometer = 9.6 miles, incandescent low beam, high beams, and marker lamps; and (4) Volvo VNL tractor, odometer = 1,144 miles, LED low beams, high beams, and marker lamps.

Taken together, these four truck brands represent approxi-

Testing Location & Setup We obtained the use of a straight and level section of concrete track at the community college’s truck driving division. This particular area is used for police training, and some tire marks were apparent, but otherwise, it was relatively clean, light gray concrete. The concrete was laid in sections with straight seams dividing each section. This area measured approximately 700 feet in length and was wide enough to accommodate two 12-foot lanes, a 3-foot shoulder for the oncoming lane, and a 6-foot shoulder for the travel (tractor) lane. We used a straight seam in the concrete to define the center of the “roadway.” Similar to Andre and Owens (2001), we took measurements along the following axes (see Figure 1 below): Axis 1: (Line 1) Outside edge of oncoming shoulder (-21 ft; 21 ft left of vehicle midline) Axis 2: Outside edge of oncoming lane (-18 ft) Axis 3: Center of oncoming lane (-12 ft) Axis 4: Center of roadway (-6 ft) Axis 5: Center of travel lane/vehicle midline (0 ft) Axis 6: Outside edge of travel lane (+6 ft; 6 ft right of vehicle midline) Axis 7: (Line 7) Outside edge of travel lane shoulder (+12 ft) Procedure We started illumination measurements at 21:15 CDT on March 12, 2020, when the sun was 41.3° below the horizon at an azimuth of 278.2°. Trucks were oriented approximately due east (90°) during testing. We measured the pre-existing ambient illumination to be 0.00 lux (measurement made at ground level 50 ft from the first truck’s front bumper with the meter facing up).

We recorded a total of 28 measurements for each of the four trucks: Along each of the seven axes, we measured civil twilight distances at both ground level and at an elevated position 42 inches above the road surface (see Andre & Owens, 2001). Measurements were made for both low and high beam headlamps while the truck engines were idling.

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Figure 1: Drone view of the testing site. The photo was taken from an elevated position behind an exemplar truck looking east. Lane markings were superimposed afterward: Outer edges of shoulders (axes 1 & 7) are black, lane fog lines (axes 2 & 6) are white, lane centers (axes 3 & 5) are blue, and the center of the roadway (axes 4) is double yellow.

Once a truck was aligned correctly with the roadway’s end, we cleaned its headlamps using a window cleaning solution. Truck headlamps were then turned on low beams.

We then walked the light meter forward away from the truck along each of the seven axes until we obtained a reading of 3.3 lux. If there was no location on the axis where the minimum of 3.3 lux was reached, we collected no data.

Notably, the light meter’s sensor was held facing a given axis’s origin and not facing the truck’s midline. We then used a roller wheel to measure the distance to the reading’s location along each axis, recorded those distances, and placed a semi-permanent marker at each location. The procedure was repeated with the high beam headlamps illuminated.

Locations were verified the next day using the robotic total station.

Empirical Findings Figure 2 shows a top-down photograph of the low-beam illumination provided by one of our exemplar vehicles. We have superimposed the roadway lanes’ locations to illus32 Collision Magazine - Volume 15 Issue 2

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trate the characteristics of CTDs in the real world. Note that location “A” shows substantially less illumination in the oncoming lane than the travel lane and its shoulder.

Location “B” shows that headlamp illumination extends farther in the travel lane than the oncoming lane. These patterns were similar for all vehicles tested.

To better compare CTDs amongst different beam intensities and recording locations, we created Figure 3, which shows both low beam and high beam data measured at ground level (left panel A) and at 42 inches above the roadway (right panel B). We included mean CTD data along with the farthest and shortest truck data for each elevation and beam intensity. Note that the “farthest” and “shortest” performers were not necessarily the same vehicles across conditions.

Low-Beam Comparisons The lower three functions in both panels of Figure 3 show the farthest, mean, and shortest CTD functions measured along the seven axes. At ground level (panel A), average

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Figure 2: Overhead drone photo of exemplar truck’s headlamp illumination. Two-lane roadway lines are added for perspective.

CTD at the vehicle midline (center of the travel lane) was 221.4 ft. Distances were shorter in the oncoming lane and farther on the travel lane’s shoulder. This pattern held for the “farthest” and “shortest” trucks.

Average CTDs were shorter when measurements were taken above the road surface (see Figure 3 panel B, lower three functions). The average CTD at the vehicle midline was 121.8 ft or approximately 55% of the CTD average at ground level. The data recorded at the elevated location did not follow the same shape as the ground-level data.

Both measurement positions showed a large amount of variability among trucks’ CTDs along each axis. At ground level, the amount of variability was relatively constant across the seven axes. At the elevated location, only one truck had a measurable CTD to the left of the roadway’s center line, and that was in the center of the oncoming lane. Illumination did not reach 3.3 lux at either the oncoming lane’s edge or its shoulder’s edge.

High-Beam Comparisons The upper three functions in both panels of Figure 3 show the farthest, average, and shortest CTD functions for high-beam headlamp illumination. At ground level (panel A), the average CTD at the vehicle midline was 571.9 ft.

Again, mean distances in the oncoming lane were shorter,

but individual truck data was more variable in terms of the shape of their high beam CTD functions.

At the elevated position, high beam CTDs across the seven axes were relatively stable. The average at the vehicle midline was 520.9 ft or approximately 91% of ground level CTD. Thus, the drop-off at the elevated position was less severe than that found with low beams, likely because high beams “fill-in” the area above the roadway. Interestingly, the variability was the greatest at the high-beam elevated measurements, indicating more considerable differences in the headlamp beam patterns’ three-dimensional shapes.

The advantage of using high-beam headlamps is evident in both panels of Figure 3. High-beams added an average of 352.2 ft to the CTD at ground level, and at the elevated position, the added distance averaged 420.6 ft. Quantified differently, low-beam ground level CTDs were on average 35.7% of high-beam CTDs. At the elevated position, lowbeam CTDs were on average only 17.5% of high-beam values.

Discussion We performed the present study to find information about commercial truck headlamps which could be used to educate drivers about the capabilities of their headlamp systems. The present study quantified low beam and high

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Figure 3. Civil twilight distances measured at ground level (panel A) and 42 inches above the roadway (panel B). Two-lane roadway lines are added for perspective. Key: LB = Low beam, HB = High Beam, Far = truck with the farthest average, Avg = average of all trucks, and Short = truck with the shortest average. Error bars = +/- 1 SEM. beam headlamp illumination in terms of civil twilight’s dark limit. Low-beam, ground-level CTDs were similar to those reported in a market-weighted passenger vehicle sample (Andre & Owens, 2001). Other characteristics of the trucks’ beam patterns include farther distances on the travel lane’s outside edge and shoulder and reduced distances in the oncoming lane. These characteristics are the result of slightly downward and rightward aimed headlamps that are consistent with federal standards in the United States to control glare for oncoming drivers. Un34 Collision Magazine - Volume 15 Issue 2

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fortunately, this pattern has consequences to the detection and recognition of hazards depending on the hazard’s location relative to the headlamp beam. For example, we measured useful illumination to approximately 180 ft in the oncoming lane center compared to 221 in the travel lane center. Thus, detection and identification of unmarked hazards (e.g., road debris, animals, pedestrians) entering the roadway will be more probable if the object is entering from the right side of the road.

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At the elevated position, low-beam CTDs were substantially shorter than their ground-level counterparts. Along the trucks’ midline the elevated CTD was only 55% the ground-level distance. This is likely due to the beam pattern’s conical nature (see, e.g., Figure 2 in Andre & Owens, 2001), where most of the beam’s intensity is directed towards the ground. Consequently, useful illumination used to detect elevated hazards extends to shorter distances.

Additionally, the farthest and mean functions exhibited a sharp reduction in CTD at the travel lane’s edge. This was due to the measurements from one individual truck, and was likely due to that particular headlamp’s optics. The reduction remained apparent in the overall mean because we tested only four vehicles.

High-beam CTDs were obviously farther because of the added intensity and that regulations allow for high-beam headlamps to be directed upwards relative to low-beam.

The upward aim extends the high-beam CTDs significantly in the elevated position to approximately four times the low-beam distance. Further, no measured high-beam distance was less than a low-beam distance regardless of elevation. Thus, their additional illumination and farther CTDs are advantageous to drivers, and we recommend their use whenever conditions permit.

We would be remiss if we did not contextualize our findings. First, it is essential to note that we recorded our data under optimal experimental conditions: new, clean headlamps measured during clear weather conditions.

Headlamp factors such as age, lens clouding, and aim, and environmental factors such as rain, dust, and fog will diminish CTDs. Therefore, we should be clear that CTDs are specific to the driving conditions which exist at a given moment and can change accordingly.

Second, a CTD analysis does not guarantee that an object will be seen if illumination is at 3.3 lux or higher. Indeed, Sivak, Helmers, Owens, and Flannagan (1992) suggested that illumination levels in the middle of civil twilight might be a better benchmark value. Object detection depends on many factors associated with both the driver (observer) and the object itself, and U.S. highway systems provide other cues to assist the driver understand what lay ahead. These include roadway lighting, reflective lane delineators and road markings, reflective signs, and other vehicle lighting.

Third, the similarities to Andre & Owens’ (2001) civil twilight distances, while interesting, do not extend to this class of vehicles. Here, we tested commercial vehicles, while Andre and Owens measured passenger cars. One consequence of this distinction is that while CTDs may be similar, stopping distances are not. The assured clear

distance ahead (ACDA) concept states that a driver should be able to stop within his/her “range of vision” (Leibowitz, Owens & Tyrrell, 1998). Thus, drivers should not “overdrive” their headlamps’ functional illumination when traveling at night. In the absence of other sources of artificial illumination, we can use a headlamp system’s CTD to define this “range of vision” distance based on functional, or useful, illumination. Importantly though, consider two vehicles traveling 65 mph with headlamps that provide similar CTDs (for example, 225 ft). One is a passenger vehicle (3,500 lbs), and the other is a loaded tractor-trailer (80,000 lbs). If the vehicles are not in a full skid (eg., hockey puck on ice), the tractor-trailer will more often than not require a greater distance to stop, yet the ACDA distance remains the same for both vehicles. Therefore, to abide by this rule, commercial vehicle drivers would have to go slower to reduce their stopping distances.

Unfortunately, ACDA standards are paradoxical in practice: while many states have established an ACDA standard as a basis for safe driving (a logical, reasoned decision), they still post recommended speed limits above night driving ADCA speeds. For example, when night driving with low beam headlamps, a passenger vehicle can only travel a maximum of approximately 45 mph to stop within a CTD-inspired distance of 230 ft (assumes 0.75 g deceleration and a 2.0 sec perception-reaction time).

Such behavior would prove dangerous on limited-access highways with higher posted speed limits, and commercial vehicle drivers would have to travel even slower because of greater stopping distances.

Conclusion Our goal was to educate commercial vehicle drivers about their headlamp illumination as defined by how the human visual system functions in low light conditions. We would not expect unmarked hazards to be identified beyond the CTDs reported here, namely 221.4 ft with low beams and 571.9 ft with high beams headlamps, since beyond these distances at ground level, there is no useful illumination from truck headlamps. We would recommend the creation of data-driven training programs which educate drivers especially about distances traveled as a function of vehicle speed (in feet/sec), stopping distances under a range of conditions, and civil twilight distances.

Cited References Andre, J. & Owens, D. A. (2001). The Twilight Envelope: A user-centered approach to describing roadway illumination at night. Human Factors, 43, 620-630.

Andre, J. & Rogers, S. (2006). Using verbal and blindwalking distance estimates to investigate the two-visual

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systems hypothesis. Perception & Psychophysics, 68(3), 353-361.

Francis, E. L, Tyrrell, R. A. & Owens, D. A. (2020). Perception response time and its misapplication: a historical and forensic perspective. Theoretical Issues in Ergonomics Science, 21(3), 327-346.

Held, R. (1968). Dissociation of visual functions by deprivation and rearrangement. Psychologische Forschung, 31, 338-348.

Ingle, D. (1967). Two visual mechanisms underlying the behavior of fish. Psychologische Forschung, 31, 44-51.

Leibowitz, H. W. & Owens. D. A. (1977). Nighttime driving accidents and selective visual degradation. Science, 197, 422-423.

Leibowitz, H. W. & Owens, D. A. (1991). Can normal outdoor activities be carried out in civil twilight? Applied Optics, 30, 3501-3503.

Leibowitz, H. W., Owens, D. A. & Post, R. B. (1982).

Nighttime driving and visual degradation (Society of Automotive Engineers Technical Paper Series No. 820414).

Warrendale, PA: Society of Automotive Engineers.

Leibowitz, H. W., Owens, D. A. & Tyrrell, R. A. (1998).

The assured clear distance ahead rule: Implications for nighttime traffic safety and the law. Accident Analysis and Prevention, 30, 93-99.

Owens, D. A. (1999, January). The twilight concept vs. a computer model: Alternate approaches to understanding visibility. Paper presented at the 78th Annual Meeting of the Transportation Research Board, Washington, DC. Owens, D. A. (2003). Twilight vision and road safety: Seeing more than we notice but less than we think. In J. Andre, D. A. Owens & L. O. Harvey, Jr. (Eds.), Visual Perception: The Influence of H. W. Leibowitz (pp. 69-79).

Washington, DC: American Psychological Association.

Owens, D. A. & Andre, J. T. (1996). Selective visual degradation and the older driver. IATSS Research, 20, 57-66.

Owens, D. A., Francis, E. L. & Leibowitz, H. W. (1989).

Visibility distance with headlights: A functional approach (Society of Automotive Engineers Technical Paper Series No. 890684). Society of Automotive Engineers. Warrendale, PA. Owens, D. A. & Tyrrell, R. A. (1999). Effects of luminance, blur, and age on nighttime visual guidance: A test of the selective degradation hypothesis. Journal of Experimental Psychology: Applied, 5(2), 115-128.

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Schneider, G. E. (1967). Contrasting visuomotor functions of tectum and cortex in the golden hamster. Psychologische Forschung, 31, 52-62.

Sivak, M., Flannagan, M., Kojima, S., & Traube, E. C. (1997). A market-weighted description of low-beam headlighting patterns in the U.S. (Tech. Report No. UMTRI-97-37). Ann Arbor: University of Michigan Transportation Research Institute.

Sivak, M., Helmers, G., Owens, D. A., & Flannagan, M. (1992) Evaluation of proposed low-beam headlighting patterns. Report No. UMTRI-92-14. University of Michigan Transportation Research Institute: Ann Arbor.

Trevarthen, C. B. (1968). Two mechanisms of vision in primates. Psychologische Forschung, 31, 299-337.

Acknowledgments We would like to thank the following individuals for invaluable help on this project: Dale Runkle (CDL driver), Martin Garsee (Facilities Operations Manager, Houston Community College), and Robert Swint (CEO, ATA Associates, Inc.). We are incredibly grateful to International Trucks of Houston, Rush Truck Center of Houston, Vanguard Truck Center of Houston, and Houston Freightliner & Western Star. We would also like to thank Thomas Bohan and Richard Tyrrell for their helpful suggestions on earlier versions of the manuscript.

Author Bios Jeffrey Andre earned his Ph.D. in experimental psychology (visual perception) in 1995 from The Pennsylvania State University. He is currently a Professor of Psychology at James Madison University, Harrisonburg, VA. He is also the sole proprietor of Contrast Associates, LLC, a visual perception/human factors consulting firm based in Moseley, VA. April Yergin earned her B.S. in Aeronautical Engineering Technology in 1986 from Arizona State University and an ACTAR certification in 1997. She is currently a senior accident reconstructionist and analyst at ATA Associates, Inc. in Houston, TX, working frequently on heavy truck accidents.

Allen Vaughan earned his B.S. degree in Mechanical Engineering from Wright State University. He is currently a staff engineer at ATA Associates, Inc. in Houston, TX. He is also an accident reconstructionist who works frequently in automobile accidents, including heavy truck and conspicuity related accidents.

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_____Drag_Sled_Mechanics:_ Fundamental Theory and Experimental Verification Frank Navin, Ph.D., D.Sc (Hon), Professor Emeritus, University of British Columbia

I

ntroduction Measuring a tire-pavement’s coefficient of friction is simple in theory but very complex in practice. Major studies have concentrated on wet pavements that usually present the most difficult driving conditions. Studies by Wambolt et al (1995) and a New Zealand study by Wilson and Black (2009) illustrated the complexity of friction measurements on wet highway pavements. The problem is about as difficult on dry pavements. Some of the easily measured factors to be considered are shown by bold type in Figure 1. The details will be presented in the next section. This paper will concentrate on the needs of collision reconstruction measurements on dry asphalt pavements.

stitutions and funding agencies. See Appendix A for the participants in this research.

There was a time during the late 1990s and into the 2000s when the collision reconstruction community was embroiled in the “drag sled wars”. Those questioning the drag sled included Bartlett, Baxter, Liversay, Standard and Wright (2006). Supporters included Calhoun, Dinitto, Biller, Cumming and Osterhoult (2010) and Navin and Musa (2004). Recently a paper by Navin (2019) gave a better definition of drag sled mechanics. The dispute continues to stimulate debate in professional circles and various courts of law.

Using a drag sled during collision reconstruction requires the consideration of two independent systems.

First is the drag sled and the second is the braking (skidding) vehicle. The following outlines the nature of the two systems.

This research represents over 20 years of periodic effort by the author and about 40 other professional reconstructionists. Funding and in-kind support was roughly $150,000 CDN provided by a number of in-

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The experiments reported here started in about 2002.

Aim The main purpose of this paper is to present a theoretical and empirical relationship between a drag sled’s; weight, pulling force, and pull angle; the definition of coefficient of friction and the drag factor.

Another aim is to illustrate that a drag sled, properly used, is an acceptable device to measure a drag sled’s coefficient of tire-pavement friction.

Study Method

Drag Sled Experiments Prior to the author’s drag sled study, vehicle skidding was mainly for police purposes to “calibrate” drag sleds. There was a major push at WREX2000 that demonstrated that “pullers” had to be trained to successfully use a drag sled.

In the early 2000s, the author organized a Latin Square experiment carried out by well-trained Royal Canadian Mounted Police (RCMP) collision reconstructionists. The results were reported in Navin et al

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2009 at IPTM. The main result was the recognition that a manufactured drag sled could reduce much of the variance observed between drag sleds and users.

The information in Figure 1 was used to minimize the number of factors for the vehicle skidding experiments. The following were the factors:

A recent theoretical analysis of the drag sled used the “classical point mass system” to derive a general equation. The equation includes the pulling force, the drag sled weight, pull angle and the definitions of coefficient of friction and drag factor. The equation defines the relationship of the drag factor to the pull angle and the coefficient of friction for any pavement. The details will be presented in a later section.

1. Vehicle mass, measured as load per tire;

Vehicle Experiments Based on extensive research of the scientific and collision reconstruction literature and limited observations for police skidding tests, the author hypothesized that there was some relationship between a vehicle’s deceleration rate and tire load and speed. At the time, during the early 1980s, the cost of suitable research accelerometers was prohibitive. The simple mechanical and visual observations were not adequate to make the required measurements. The G-analyst (1980) changed the experimental procedure by providing a reasonably priced accelerometer but it had limited data collection. An early study by Reed and Kelsin (1987) gave accelerometer results from emergency braking.

Another example is the experiment by Eubanks et al (1993) of vehicle skidding on a concrete runway. It gave detailed and redundant skidding distances and accelerations for the same vehicle. Other studies by Ruller (1993) in Australia for asphalt gave results of similar precision. An excellent study by Heinrichs, et al (2004), illustrates the instrumentation needed for a truly scientific experiment.

The availability of the Vericom family of accelerometers dramatically changed how police braking experiments were undertaken and the precision of the results. The data collected included time, distance, speed, and acceleration. Reconstructionists could now look at the details of the braking during the entire process.

In early 2000s, the author was asked by IPTM to study the relationship between a skidding vehicle and a drag sled. Based on previous research, it was decided to do a detailed study of braking (skidding) vehicles that were equipped with redundant measuring devices when possible.

2. Speed at the start of visible skid marks; 3. Asphalt pavements that were; clean, level, dry and worn or weathered; 4. Vehicles that were well maintained and had good tires; 5. Drivers that were usually police trained and familiar with emergency braking; 6. Vehicles equipped with accelerometers or a 5th wheel to measure deceleration and time; 7. Visible skid marks that were measured by competent police trained reconstructionists; and 8. Experiments that were conducted during the summer under reasonable weather conditions.

Additional measurements of temperature of the tire patch and asphalt were taken but abandoned due to inconsistencies of measurements. Other standard police measuring devices such as shot markers were also used but unfortunately not consistently.

Referring back to Figure 1, it is easily seen that the experiments did not include all possible factors but an attempt was made to have reasonably reproducible experimental results that were easily measured or observed.

Results Drag Sled’s Theoretical Relationship The forces acting upon a sliding body on a firm surface is shown in Figure 2. There are three forces in this case; the weight acting down, the pulling force acting at angle θ and the resultant resisting motion. In this particular case, they all act through a single concurrent point on the surface of the pavement-tire interface. The coefficient of friction on a firm surface is defined as the force resisting motion (F) divided by the normal force (N), see Figure 2. Mathematically the equation is:

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Figure 1: Factors Influencing Tire-Pavement Coefficient of Friction

Figure 2: Drag Sled, as a Three Force System Being Pulled on a Firm Surface 42 Collision Magazine - Volume 15 Issue 2

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Figure 3: Theoretical Drag Factor and Pull Angel

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Inverting W/P gives, by definition, the Drag Factor, DF and the equation is:

Where; P = Pull Force in pounds or kilograms, W = Drag sled weight in pounds or kilograms, θ = Pull angle in degrees, and

Figure 4: Drag Sled’s Kinetic Pull Force and Pull Angle Where h = height to the pull point, and

DF = Drag factor = P/W. This is the fundamental coefficient of friction equation for a drag sled on a firm surface.

l = distance, centre of mass to pull point.

The coefficient of friction at this point is;

Rearranging the equation gives the following drag factor equation.

In this equation, the only variable to be measured is P, since both W and α are predefined.

The drag factor has three limiting conditions and one condition of interest.

1. At a pull angle of 0 degrees it is µ, the coefficient of friction, 2. At a pull angle of 90 degrees it is 1, and 3. Between these two values, there is a minimum value at a pull angle of φ which is;

The theoretical relationship between the drag factor, the pull angle and the coefficient of friction is shown in Figure 3. The lowest value is what is expected on ice. The expected value for a smooth concrete pavement such as a shop floor is between 0.5 and 0.6. The coefficient of friction range of most interest for collision reconstruction is between 0.7 and 0.9.

The previous analysis is for the kinetic coefficient of friction. The same analysis holds for a peak coefficient of friction.

Empirical Relationship

The condition of interest is the one illustrated in the drag sled’s free body diagram in Figure 2. All three forces are coplanar and converge to a concurrent point in the plane at the interface of the tire and pavement. In this condition there are only forces and no moments. This is known as a three force static equilibrium condition. The pull angle to achieve this condition is based on the geometry of the drag sled and is given as;

The empirical relationship between the kinetic pull force P and the pull angle θ is shown in Figure 4. This diagram is based on data and the author’s adjustments. The two surfaces are worn asphalt (blue, Florida HP-FLAIR, 2021, 0 to 80 degrees) and Smooth Concrete (green and brown, Navin-RCMP 2004, 0 to 50 degrees, see Navin (2010).

The heavy drag sleds are Braker Box and the light sled is a homemade, flat sled.

There is a relationship between the drag sled’s bearing area and the pull angle. The drag sled’s bearing area ranges from 100% to 85% for pull angles of 0 to 40 degrees.

Coefficient of Friction

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A 3-D view of the fundamental coefficient of friction relationship for a Braker Box drag sled on dry level Asphalt surfaces is shown in Figure 7. Note that the basic shapes of Figures 4 and 5 are on the appropriate planes. The relationship on the Drag Factor-Coefficient of Friction plane should be a straight line.

First, it shows a theoretical relationship between the drag sled’s drag factor (DF), it’s tire-pavement coefficient of friction µ and the pull angle θ. This equation is based on the classical point mass system. The conditions to use the point mass have been met in the pull angle range of 0 to 30 degrees.

Figure 5: Drag Sled’s Coefficient of Kinetic Friction, Pull Angle

Second, it illustrates the drag sled’s empirical relationship between kinetic pull force P, pull angle θ, drag sled weight W, and the pavement surface.

Finally, the measurement of a drag sled’s coefficient of friction has a few rules. A proposed rule suggests that the pull angle’s new “sweet spot” be set to satisfy a three force system in static equilibrium. This implies that the only variable under the user’s control is the pulling force. Also, the unique pull force angle has no moments, only forces.

Braking Vehicle Empirical Observations

Figure 6: Observed and Theoretical Drag Sled Results The drag sled’s coefficient of kinetic friction is calculated using the Pulling Force observed and adjusted data in Figure 4. In an ideal world the coefficient of friction shown in Figure 5 would have a constant value over the range of pull angles. This is true up to about 50 degrees, see Figure 6. The point mass analysis assumes that the drag sled is flat on the pavement at all times. This assumption holds well for pull angles between 0 and 40 degrees.

Observation and Theory Observation and theory fit very well between the pull angles of 0 to 50 degrees. Figure 6 shows the data and theory for a worn asphalt pavement and a Braker Box drag sled.

Beyond 50 degrees, the observed average pull force was slightly underestimated.

The usual pull angle is between 15 and 25 degrees. Observation and theory fit well within the pull angle range.

Conclusions This brief analysis provides answers to a few long standing questions about the validity of drag sleds.

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There is no single equation based on first principles to explain the many parameters involved in a braking pneumatic tire. In the absence of such a relationship, a set of empirical observations were undertaken with the constraints as outlined previously. Two major tests were run in the summer of 2004 and 2008. The summary data is in Appendix B. The preliminary analysis developed a complex relationship between tire load (L), speed at the start of skidding (SSSM) and the vehicle’s dynamic coefficient of friction (f(v,s)).

The work by Limpert (1978) and Ruller (1993) were influential in selecting speed at the start of skidding as a variable but later changed to skid distance. The original equations and the analytical methodology are reported in IPTM , Navin el al (2010) and an SAE presentation (Navin 2011).

The following is a revised methodology that provides a more intuitive approach for collision reconstruction.

Locked Wheel Braking The basic relationship uses observed data to estimate the vehicle’s dynamic locked-wheel braking during skidding.

The data were; the vehicle average tire load and the length of visible skid marks.

There are two parts to the data needed for the equation.

The first part is for a slow speed pull to approximate the

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value of f(v,s) just as it starts to move. The second part of the data gives f(v,s)for the visible skid marks at various speeds ranging from about 20mph up to about 60mph. The data were collected by the author and some from Heindrick et al (2004). See Appendix B for the detailed observations.

The multiple linear regression relation (source: Statistics Kingdom, web 2021) between; the deceleration of locked wheel sedans [f(v,s)], pneumatic tire average load of (L), and visible skid marks (SM) is given with the following equation;

Where f(v,s) = dynamic deceleration of a skidding vehicle, L

= tire load, 1000 pounds

SM = visible locked wheel skid marks in feet.

Figure 7: 3-D View of a Drag Sled’s Relationship on Dry Level Asphalt for: Drag Factor, Coefficient of Friction and Pull Angle

The statistical R2 is 0.882 and the adjusted R2 is 0.877. The equation explains 88.2 percent of the variance of the dynamic deceleration of a skidding vehicle. Figure 8 illustrates the data set and the resulting f(v,s) equation's surface.

A regression equation to estimate a vehicle’s dynamic coefficient of friction derived by Heinrichs et al (2004) gives similar results in the f(v,s) – SM plane with a similar R2 value. They estimated the 95 percent confidence interval for f(v,s) to be about ± 0.04 for skid distances of about 100 ft.

Estimating the speed at the start of visible skid marks is simply;

Where SSSM = Speed at the start of skid marks, mph.

The linear regression equation for the speed at the start of braking is;

Where SSB = Speed at the start of braking in mph.

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the speed at the start of vehicle braking. Neptune et al (1995) derived similar results.

In general, the vehicle dynamic skidding has a small but measurable influence due to tire load (L). There is a significant influence of the visible skid distance. The biggest influence is in the lower range, below skid marks of about 30 ft. There is probably an upper limit of the regression model of vehicle skidding since many of the experiments at speeds above about 60mph, (roughly 150 feet skidding) resulted in flat spots on the vehicle tires. The author had no data above 60mph.

The regressing equations derived from the data are very robust.

drag sled value at a 0 degree pull angle is the coefficient of friction. In the case of the Braker Box drag sleds used for the development of the Drag Sled Pull Angle relationship, the two coefficients of friction, drag sled and vehicle are identical. The three force system value is measured at the angle α.

Condition one: If the drag sled's kinetic coefficient of friction, CoF(k) at θ=0 and at θ=α are similar to the 0.87 value reported for the experimental worn or weathered asphalts, then the f(v,s) estimated from the regression equation is the value to use to estimate the speed at the start of skidding and the speed at the start of braking.

Condition two: If the values are not similar then adjustments to the relationship must be made. The main asRelationship of a Drag Sled and a Skidding Vehicle sumption is that the relationship between a vehicle's L, SM The drag sled may be used for one of two purposes. The and f(v,s) is constant for any firm surface of interest. first purpose is to verify that the pavement is worn or Calibrating a Drag Sled and Pavement weathered asphalt similar to that used to derive the previous equations. The second purpose is to establish the coef- There are four calibration cases to be considered. The cases ficient of friction and the drag factor. are outlined in Table 1. To carry out calibration requires a standard for comparison. In this study it is assumed there The proposed empirical relationship between the two in- are two standards for the estimate of a vehicle’s dynamic dependent systems; a drag sled and a skidding police ve- deceleration. hicle is illustrated in Figure 9. As pointed out earlier, the

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The first standard or “truth” is the relationship developed for a vehicle's f(v,s) as a function of L and SM on a worn or weathered level asphalt pavement. The relationship of f(v,s) to L and SM on a worn or weathered asphalt pavement is assumed to remain constant as the coefficient of friction changes for different firm, clean highway pavements.

The second standard or “truth” is the drag factor (DF) with the pull angle θ and coefficient of friction µ has a similar shape for all firm, clean highway pavements. The drag factor is assumed to go up or down with the changing coefficient of friction on a firm, clean highway pavement. The drag sled results reported here used Braker Box drag sleds manufactured between about 2000 and 2015.

PAVEMENT DRAG SLED

Known

Unknown

Case #1

Case #2

Unknown Case # 3

Case #4

Known

Table 1. Calibration Conditions

Case #1 This case requires no calibration since the pavement is known to be worn or weathered asphalt (Truth #1) and the drag sled behaves like a Braker Box sled (Truth #2).

This is shown by the solid line in Figure 10.

The f(v,s) can be calculated according to the previous regression equation.

Case #2 In this case, the drag sled is known (Truth #2) and the pavement is unknown. The drag sled is used to measure the coefficient of friction. The f(v,s) curve is adjusted up or down according to the value of the coefficient of friction.

Curve C is for a smooth concrete shop floor.

An alternative calibration method is a process similar to the experimental process outlined in this paper. A suitably instrumented police style vehicle (weight and tires) should be skidded or pulled. About five repetitions at the same speed will establish a good relationship between a single speed and f(v,s).

Figure 10 curves, A and B, are for two different pavements.

Curve B is adjusted to the value of the same vehicle being pulled at a low speed on the same smooth concrete pavement as curve C. The result is similar to that of the as-

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phalt. Curve A is skidding on a different smooth concrete pavement. There is a small discrepancy with curves B and C. This illustrates that the experiments should be carried on the same pavements.

Case #3 The following procedure assumes that the test surface is similar to that of worn or weathered asphalt (Truth #1) used in the reported experiments. The purpose is to calibrate a personal drag sled and is shown by D in Figure 10 for a new Braker Box on asphalt.

Any new drag sled, including Braker Box drag sleds, should be calibrated. The drag sled to be calibrated should be pulled 10 times at angles of 0, 10, 20, 30 degrees and at α(3FS) as shown in Figure 10. The value of the drag factor and the coefficient of friction should be estimated.

The most stable value is the coefficient of friction and that value is used to recalculate the plotted drag factor.

The new drag sled’s estimated coefficient of friction is 0.96 compared to the low speed f(v,s) of the skidding police car which is 0.87. The ∆.DS is 0.09. To match Truth #2, it is necessary to subtract 0.09 from the new drag sled's drag factor. The revised value of f(v,s)R is;

Case #4 This last situation is similar to the conditions at the start of this research. In this case, the nature of both the pavement and the drag sled must be established.

Speed Calculations The drag sled’s drag factor may be used in one of two ways.

The first method is to estimate the f(v,s) value using the observed L and SM values. This gives an “exact” estimate of speed before braking.

The second method is based on an agency’s policy on how to estimate speed. In this case, the value of the drag factor is selected such that the speed at the start of skidding represents a very conservative value. The conservative value DF (or f(v,s)) is set by specifying the pull angle θ to be used.

Special Note The approach to estimate a vehicle’s dynamic coefficient of friction was suggested by Limpert in 1989. He used vehicle speed rather than skid distance. His equation was;

Where f = approximate peak coefficient of friction SSB = Speed at Start of Braking, mph

The analytical procedures outlined in this paper should establish the drag sled as a viable method to estimate a tire-pavement coefficient of friction.

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Fz = Dynamic tire load, lb, and N = Tire’s rated load at some tire pressure, lb It is a simple matter of converting speed at the start of braking into skid distance. Similarly the dynamic tire load and rated load could be transformed into the average load per tire. The revised equation is:

Notice that this equation has a linear relationship with tire load and non-linear with the skid distance. The extension of Limpert’s 1989 equation only needed further refinement of testing instrumentation to be transformed into easily available variables such as skid marks and tire load.

Conclusions This theoretical analysis of the drag sled presents some unique characteristics of the drag sled. The first is the relationship between the three force equilibrium conditions at the unique angle α. At the angle α, the forces of Mass (weight), Pull and Reaction all converge at a single coplanar concurrent point at the interface of the tire-pavement.

At this point there are only forces and there is neither

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pitch nor lift of the drag sled. Other preliminary research showed that the drag sled remains reasonably flat on the pavement surface for pull angles between 10 to 40 degrees.

The drag sled’s tire-pavement coefficient of friction, both peak and kinetic, may be estimated by the drag sled. The coefficient of friction is the DF at a pull angle of 0 degrees.

The coefficient of friction can also be estimated knowing the DF and the angle of pull.

The standard or “truth”, for the dynamic coefficient of friction, f(v,s) of a skidding vehicle is given by a multinomial relationship with the average load on a tire L and the visible skid marks SM. The R2 value for the equation is 0.88, which explains 88 percent of the variation in f(v,s). The relationship is assumed to apply to similar firm, level pavements such as concrete and smooth concrete shop floors.

There is an empirical relationship between the DF, pulling angle and police vehicles (L=1.25 1000lb/tire) and visible skid length. This empirical relationship may be used to calibrate a drag sled.

Finally, given the data of L and SM, the DF’s f(v,s) value may be used to estimate the vehicle’s speed at the start of braking. The calculation of the speed at the start of braking is a simple linear regression model with an R2 value of 0.99, almost an exact relationship.

It should also establish the drag sled as an acceptable means to estimate either a vehicle’s dynamic coefficient of friction or a policy drag factor for conservative speed estimates at the start of skidding.

3. Eubanks J., Haight W., Malmbury R. and Casteel D., “A comparison of devices used to measure vehicle braking deceleration”, SAE Paper 930665. 1993.

Also, the DF value may be set by policy to insure a con- 4. Heinrichs, B. E., Allin, B. D., Bowler, J. J., Siegmund, G. P., Vehicle speed affects both pre-skid braking kiservative speed estimate by specifying the pull angle. This nematics and average tire/roadway friction, Accident last approach assumes that the characteristics of the drag Analysis and Prevention 36 (2004), on the web sled are known.

The analytical procedures outlined in this paper should establish the drag sled as a viable method to estimate a tirepavement coefficient of friction. It should also establish the drag sled as an acceptable means to estimate either a vehicle’s dynamic coefficient of friction f(v,s) or a policy drag factor (DF) for conservative speed estimates at the start of skidding.

References 1. Bartlett, W., Baxter, A., Livesay, B., Standard, T., and Wright, W., ”Comparison of Drag-Sled and SkiddingVehicle Drag Factors on Dry Roadways,” SAE Technical Paper 2006-01-1398, 2006.

2. Calhoun D., Dinitto G., Biller B., Cummings J., and Osterholt G., “Repeatability and Reliability of Drag Sled Testing”, 2010, web paper accessed September 2018.

5. Limpert, R., Motor Vehicle Accident Reconstruction and Cause Analysis, Michie Co, Charlottesville, Va., 1978.

6. Limpert, R., Motor Vehicle Accident Reconstruction and Cause Analysis, Third Edition, Michie Co, Charlottesville, Va., 1989, Equation 18-6 7. Navin, F., Musa, P., Analysis of Data from the Drag Sled Experiment Conducted on February 17, 2003 at Palm Beach County Florida, SYNECTICS RSR Corp, Vancouver, BC, June 2004.

8. Navin, F., Drag Factor and Relationship to the Coefficient of Friction, Institute for Police Technology and Management, Jacksonville FL, April 2005.

9. Navin, F., Brewer, E., Baerg, R., “Drag Sled and Automobile Skidding on Dry Asphalt: An Experimental Relationship,” 20th Canadian Multidisciplinary Road Safety Conference, Niagara Falls, ON, June, 2010.

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10. Navin F., “Drag Sled PROTOCOL 2010”, IPTM Special Problems, Jacksonville, FL, 2010.

11. Navin, F., “Drag Sled and Automobile Skidding; an Analytical Relationship for Inline Locked Wheel Braking”. Presented at the Society of Automobile Engineers, Annual Meeting, Detroit, Michigan, April 2011.

12. Navin. F., Drag Sled Mechanics; What does it Measure? Canadian Multidisciplinary Collision Research Conference, Calgary AB, June 2019.

13. Neptune, J., Flynn, J., Chavez, P., Underwood, H., Speed from Skids: A Modern Approach, SAE Paper 950354, 1995.

14. Reed, W., Kelsin, A., A., Vehicle Response to Emergency Braking, SAE Paper 87051, Warrendale, PA, 1987 15. Ruller, J., The Evaluation of Surface Skid Resistance and Methods for Determining Such Resistance, Faculty of Built Environment and Engineering, Queensland University of Technology, Brisbane, Australia, 1993 16. Ruller, J., A Study of the Effect of Vehicle Mass on Skid Distance, Vehicles Emergency Braking, Accident Investigation Squad, Queensland Police Service, Brisbane, Australia, July 1993 17. Wambold, J, C., Antel, C., Henery, J., and Rado, Z., International PIARC Experiment to Compare and Harmonize Texture and Skid Resistance Measurements, Final Report, World Road Association (PIARC), Paris, France, 1995.

18. Wilson, D.J., and Black,P.M., The skid resistance performance of natural New Zealand aggregates using a dynamic friction tester, Quarterly Journal of Engineering Geology and Hydrogeology, 42, 61–72 DOI 10.1144/1470-9236/07-072, Geological Society of London, 2009, web access July 2021 19. The statistical analysis used Statictics Kingdom, web access 2021

APPENDIX A Selected List Of Participants This project has been ongoing for many years. The origin goes back to the early 1990s when Dennis Payne, formally of the Florida Highway Patrol (FHP) and The Institute of Police Technology and Management (IPTM), raised a question about observations and theory of braking on downgrades and drag factors. This issue was pursued more vigorously starting in the late 1990s. The following individuals helped over the years it took to complete the new experiments that form the bases for this paper.

The testing in 2004 at Chilliwack, British Columbia (BC) and 2008 in Prince George, BC were supported by the Royal Canadian Mounted Police (RCMP) “E” Division Traffic Services, Innovative Vehicle Testing Ltd. (IVT) and MEA Forensic Inc. The University of North Florida’s IPTM provided some financial support as did Synectics Road Safety Research Corp and the author’s Natural Science and Engineering Research Council research grants at the University of British Columbia (UBC). Testing facilities were provided by the RCMP’s Pacific Region Test Centre, Canadian National Railroad Centre in Prince George, BC and the BC Ministry of Forests.

Testing in California during 2009 was organized by the late M. Varet of KEVA Engineering at their facilities. Officer J. Grindey of California Highway Patrol helped with the drag sled experiments. Florida tests in 2009 and 2010 used drag sleds provided by the Florida Highway Patrol (FHP). The facilities were arranged by IPTM and other details by Sargent Jimmy Starling of FHP. Testing at the IAARS 2009 meeting in Boise, Idaho was arranged by the Idaho State Police. Testing at the IATAI 2010 meeting in Bloomington, Illinois was assisted by the Illinois State Police and Steve Neese. Additional skid testing data from 1991 to 2015 was generously provided by Rick Jobe of Vericom LLC for various VC accelerometers.

The original regression analysis used a computer package generously supplied by Analyze-it™.

A number of other individuals have contributed over the entire period of this work. They include: the late Inspector Figure 1 is information rearranged from Table 2 in “The “Bob” Rivers, Inspector Eric Brewer, and Sgt Dave WilWet Roadhold of Tyres – A European Viewpoint” by A. R. liams, RCMP; Randy Baerg, P.Eng, IVT; J. Lawerance, Williams, Dunlop Limited, Skidding Accidents; Tires, Ve- P.Eng, of MEA Forensic (Vancouver, BC); and David Brill, hicles and Vehicle Components, Transportation Research PE formerly of IPTM. Record 621, Transportation Research Board, Washington, Braker Box Drag sled tests on worn or weathered asphalt D.C. 1976 in the spring of 2021 were organized by Sargent Jimmy Starling and Master Cpl. T “Brad” White of the Florida Highway Patrol.

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APPENDIX B DATA FOR VEHICLE BRAKING ON WORN OR WEATHERED ASPHALT VEHICLE BRAKING EXPERIMENTS ON WORN OR WEATHERED ASPHALT Tire Load Speed at Start of, mph Distance, ft f(v,s) f(v,sb) Location Device 1000lb/tire Braking Skidding Skidding Braking 0.09 1 1 0.96 Florida, FHP-Tires Crown Victoria 0.09 1 1 0.95 Weight scale 0.09 1 1 0.93 0.65 59 0.77 Australia Commodore 0.65 79 0.76 Holden 0.65 104 0.75 VC200 0.65 134 0.74 2003 0.72 1 1 0.923 CA, Mazda 626 Cornell 875, Load 0.72 1 1 0.930 cell 0.83 13 6.2 1.6 6.6 0.94 0.70 17 year old Asphalt, BC 0.83 25 18.8 13.1 24.6 0.81 0.73 th Wheel MEA 5 0.83 38 30.0 39.4 59.0 0.75 0.72 1991 Honda EX-R 0.83 50 43.8 88.6 110.0 0.72 0.70 Economy, Tour, Perf 1.10 1 1 0.917 PTC, RCMP Load cell, S cam 1.10 1 1 0.920 1.10 27.9 26.6 26.9 36.9 0.80 0.69 PTC, RCMP MEA 5th Wheel 1.10 28.8 28.4 36.4 42.4 0.73 0.65 Radar 1.10 49.2 46.1 102.2 123.9 0.69 0.65 Chev Caprice 2002 1.10 51.3 48.4 108.0 128.1 0.72 0.68 Michelin LTX M/S 1.10 52.1 49.9 111.3 128.8 0.74 0.70 PTC 1.23 1 1 0.84 Load cell S cam, 1.23 1 1 0.87 Omega 1.23 1 1 0.90 1.25 1 1 0.809 PTC, RCMP MEA 5th Wheel 1.25 27.9 26.3 28.4 36.8 0.81 0.70 Chev Caprice 2002 1.25 29.7 26.3 34.3 42.9 0.77 0.68 Driver + 4pass 1.25 52.4 47.0 104.5 135.2 0.70 0.67 2004 1.25 54.4 51.1 122.1 145.6 0.71 0.67 PG, Prince George parking lot; CA parking lot;. PTC Pacific Training Center track

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APPENDIX B, continued DATA FOR VEHICLE BRAKING ON WORN OR WEATHERED ASPHALT VEHICLE BRAKING EXPERIMENTS ON WORN OR WEATHERED ASPHALT Tire Load Speed at Start of, mph Distance, ft f(v,s) f(v,sb) Location Device 1000lb/tire Braking Skidding Skidding Braking

1.26 1 1 0.806 Florida FHP Outside Parking 1.26 1 1 0.838 Lot 1.26 1 1 0.842 iLoad Pro Digital 1.26 1 1 0.875 Crown Victoria 1.26 1 1 0.861 June 2009 1.26 1 1 0.868 1.30 22.5 16.9 12.0 23.1 0.785 0.770 Prince George, BC RCMP vehicle 1.30 23.2 19.6 16.1 23.1 0.788 0.778 VC3000 1.30 24.2 20.0 16.7 26.5 0.790 0.778 Radar 1.30 24.3 20.8 18.4 na 0.779 0.768 + Shot Marker 1.30 25.1 21.7 19.4 29.3 0.800 0.772 Crown Victoria 1.30 41.4 38.2 64.8 80.3 0.734 0.737 2004 1.30 41.9 38.0 64.8 na 0.736 0.741 August 2008 1.30 44.9 40.1 72.5 92.1 0.733 0.734 1.30 47.4 41.7 77.1 105.1 0.745 0.727 1.30 56.6 48.4 109.1 154.2 0.708 0.733 1.30 57.5 56.3 151.8 174.6 0.688 0.683 1.30 59.9 55.6 139.2 163.4 0.734 0.744 1.30 1 0.68 Prince George, BC Smooth Concrete 1.30 20.3 18.0 17.8 27.7 0.600 0.670 Same as above 1.3 21.3 19.0 16.8 27.5 0.708 0.724 1.30 21.5 19.0 20.9 31.2 0.571 0.626 1.30 21.9 18.7 16.9 29.4 0.681 0.708 PG, Prince George parking lot; CA parking lot;. PTC Pacific Training Center track

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crash·ol·o·gy THE SCIENCE OF CRASHES Wesley Vandiver

Robert Anderson

Collision Forensics, Inc.

Biomechanics Analysis

Getting It Right: Analysis of a Real-Word Crash with Toyota EDR Data, Toyota Vehicle Control History, and PCS Images

C

rash reconstructionists are regularly tasked with forming opinions regarding fault and/or causation with less-than-complete information and/or evidence. The responsibility lies with the investigator to collect as much evidence as possible so that responsible conclusions can be reached. Many competing interests can interfere with an investigator’s efforts to be thorough in gathering evidence – time limitations, complexity of retrieval, management directives, case load, etc. To that end, the investigation of an incident in which partial evidence is obtained could lead to conclusions that would be changed given one more item of evidence.

both involved vehicles were disabled and police photographs were not taken. Shown in Figure 1 is the officer’s diagram depicting a right rear contact with the Chevrolet and left front and left rear contacts with the median wall. Roadway evidence such as debris or tire marks were not identified. In Figure 1, Vehicle 1 is the Lexus and Vehicle 2 is the Chevrolet.

The Chevrolet driver told the investigating officer that she was in the number three lane when the Lexus began merging into her lane and side-swiped her vehicle.

The case being discussed in this feature is an example of a crash analysis that demonstrates how vehicle damage, event data recorder evidence and Toyota vehicle control history were used collectively to solve a crash scenario with inaccurate witness statements and limited scene evidence.

The Lexus driver told the investigating officer that he was in the number three lane when he was hit and spun into the center median wall. In follow up at the hospital, the officer stated that the Chevrolet driver claimed that the Lexus suddenly changed lanes, which caused the Chevrolet to rear-end the Lexus. The Lexus driver reiterated that he was not changing lanes, but rather he was just driving in the number three lane when they rear-ended.

The general scenario of the case starts with the fact that visiting grandparents were loaned their daughter’s 2021 Lexus RX350, to drive during their stay.

That vehicle was involved in a collision in Arizona that was investigated by law enforcement.

A witness told the investigating officer that she was right behind the vehicles and saw it all happen. The Lexus was in the number two lane and began to merge into the number three lane without a blinker and into the Chevrolet.

According to the police report, at about 10 PM, the Lexus and a 2015 Chevrolet Camaro were traveling southbound on the State Route 51 in Phoenix, Arizona. It was dark with streetlights, the weather was clear, the roadway was dry and level. At the scene of the collision, the highway curved left and no unusual roadway conditions were noted. The posted speed limit was 65 mph. As a result of the collision,

With an independent witness corroborating the Chevrolet driver’s version of the events, the investigating officer concluded that the Lexus failed to maintain its lane and merged from the number two lane into the Chevrolet, as depicted in the officer’s diagram. This caused the Lexus to lose control, spinout and collide with the center median wall. The officer’s estimated speed for both vehicles was 65 mph.

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Figure 1: The officer’s diagram

Figure 2: The Arizona DOT (ADOT) traffic cam image of the crash scene www.collisionmagazine.com

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The Lexus driver and his wife were adamant that they were just driving in the lane and that they did nothing wrong. The Lexus owner found images of the crash scene in ADOT traffic cameras. As indicated in Figure 2, the ADOT photographs show the Lexus at rest with its right rear corner nearly touching the median wall. The Lexus owner also accessed the Lexus App, which showed Harsh Braking and Harsh Cornering events, as depicted in Figure 3. In this case example, the ADOT scene photograph and Lexus App did not provide sufficiently detailed information to significantly contribute to the analysis, with the possible exception that right front wheel/suspension damage to the Chevrolet is evident.

After being declared a total loss, the Lexus was inspected at Insurance Auto Auction. As shown in Figures 3 and 4, the Lexus had right-front and right-rear damage with a flat profile and abrasions characteristic with contact with the median wall. Direct contact damage to the left front was absent. The Lexus also had left rear side-swipe damage to its wheel and surrounding sheet metal as shown in Figure 6.

The Chevrolet’s right wheel damage matches the Lexus side-swipe and wheel-to-wheel contact evident during the inspection. It is noteworthy that officer’s diagram depicts the Lexus being side-swiped on the right rear side, not the

left, and the officer’s diagram shows the left front, not the right, impacting the median barrier. These discrepancies are sufficient to question the investigating officer’s conclusions, but by themselves they are not adequate to determine which driver caused the crash.

The Bosch Crash Data Retrieval Tool was used to obtain the Lexus’ CDR report. The CDR report showed that the Lexus was equipped with a 17EDR, which is a Toyota GEN4 module. Toyota airbag control modules number events that qualify for recording sequentially. As shown in Figure 7, TRG 1 through TRG 5 were stored, so that every event that that had been recorded in its service life is still being stored in its airbag control module.

The data limitations specify that there are four memory pages for front/rear/side events, four pages for side events, and two pages for rollover events. Further, when a lateral crash occurs, data may be recorded in a page for front/rear/ side and additional data may be recorded in a side crash page.

TRG 1 on Time Count 941,900 msec was a side crash with negative or right-to-left accelerations. There was not any Delta V’s, pre-crash data or a reported ignition cycle count.

Figure 3: Screen shots from the Lexus App 56 Collision Magazine - Volume 15 Issue 2

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TRG 2 on Time Count 941,900 msec was a right-side crash reporting a -1 mph longitudinal delta-V and a -11 mph (rightto-left) lateral delta-V. Longitudinal or lateral acceleration data was not reported. However, there was nearly 5 seconds of pre-crash data, including speed, accelerator pedal, brake oil pressure, steering input and yaw rate.

Figure 4: The Lexus’ right front

TRG 3 on Time Count 942,100 msec was a side crash with negative or right-to-left accelerations. Like TRG 1, there was not any delta-V’s, pre-crash data or a reported ignition cycle count.

TRG 4 on Time Count 942,100 msec was another front/rear/ side crash that resulted in a +1 mph longitudinal delta-V, and a -2½ mph (right-to-left) lateral delta-V. The second-row side airbag and seat belt pretensioner were commanded to deploy as was the passenger side curtain.

The pre-crash data is the same as that in TRG 2 with a 0.2 second time shift.

Figure 5: The Lexus’ right rear

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TRG 5 on Time Count 942,100 msec was a rollover event that reported negative (right-to-left) roll rate and lateral acceleration, as well as the same pre-crash data as TRG 4, which is shown in Figure 8.

Clearly, with all 5 events occurring within a quarter second of each other, vehicle damage and deployments matching that observed during the vehicle inspection, and only six ignition cycles between the crash and the download, it is fair to conclude that the CDR report contains data related to the subject crash.

However, all 5 events appear to be related to interactions with Collision Magazine - Volume I5 Issue 2 57

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Figure 7: The Lexus’ CDR report’s Event Record Summary

Figure 8: the Lexus’ pre-crash data from TRG 5 the median wall and not the initial car-to-car impact. Perhaps this would be expected for a vehicle-to-vehicle event that was likely the equivalent of a PIT maneuver. By itself, the CDR data is insufficient to discern which driver provided an accurate statement of the collision events.

During a post-crash vehicle inspection conducted by a private expert, Toyota Techstream was used to obtain the Vehicle Control History (VCH) from the Lexus. Looking at the Vehicle Diagnostic Report, which is like a table of contents, the last 15 VCH events, had the same odometer reading of 5447 miles. Within that group, there was a group of 8 VCH triggers within 5 seconds, as shown in Figure 9.

Combining the overlapping data from the VCH triggers, the data spanned more than 16 seconds. The car-to-car impact becomes clear when graphing the speed, accelerator pedal, longitudinal and lateral acceleration, etc. As shown 58 Collision Magazine - Volume 15 Issue 2

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in Figure 10, at the moment of impact the speed increased by about 5 mph and the accelerator pedal was released, which is characteristic of rear-end impact occupant kinematics, and then the Lexus began a left (counterclockwise) yaw while the driver steered right (counter-steered) in an attempt to regain control. The first impact with the median wall was about 3¼ seconds after the initial car-to-car impact indicated by the vertical lines in Figure 10. The span of the CDR pre-crash data is also indicated Figure 10.

As shown in Figure 11, there were also PCS images available. The column showing “icon” in Figure 9 indicates that the images were taken for steering angle speed over a certain number was detected, and a sudden turning history. The two events with images were less than a second apart and contained the same images, which are shown in Figure 12. Images 1 through 11 show the Lexus driving in the number three lane, exactly as described by its driver.

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Figure 9: The Lexus’ Vehicle Diagnostic Report excerpt

Figure 10: The Lexus’ Techstream speed from overlapping events The vehicle was traveling about 10 mph below the speed limit, and vehicles can be seen passing in the other lanes.

A collision was detected at Image 11, the Lexus pitches downward in front and begins a left yaw at Image 13. The vehicle strikes its right front on the median wall at about Image 18, and appears to strike the median wall with its right rear in Image 23.

Combined, the physical evidence, EDR data, VCH data, and PCS images leave no doubt that this crash occurred as a result of the Chevrolet striking the Lexus. The car-to-car contact destabilized the rear of the Lexus, similar to a PIT maneuver, causing the Lexus to spin out of control and strike the median barrier.

This case serves as an example of many things including, 1) a crash investigation involving modern vehicles that record

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Figure 11: The Lexus’ Techstream Read Data function showing images available

In any form of investigation, getting it right is of the utmost importance. In crash investigations, “getting it right” does not necessarily mean conclusively proving one party is at fault.

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electronic evidence, 2) a crash investigation in which the statement of a witness matched the events as described by one party, causing the investigator to adopt that driver’s statement as accurate, 3) a crash investigation where the investigator, if he/she had closely and competently examined the vehicle damage, could have recognized his/her opinions were in error without any additional evidence, and 4) an additional piece of evidence, although available, was not pursued, and when finally retrieved, clearly identifies the responsible party and exonerates the alleged party at fault.

In any form of investigation, getting it right is of the utmost importance. In crash investigations, “getting it right” does not necessarily mean conclusively proving one party is at fault. There are times when, “I don’t know” is the most reasonable and accurate answer. However, when an investigator feels that he/she can prove one party is at fault, that investigator should always take a focused look at the strength and accuracy of the available evidence, and consider what additional sources of evidence may be available that could further prove him/her correct or possibly wrong. Choosing to ignore or not pursue additional available evidence is a gamble that has serious implications on the lives of others, and in cases such as this, when the investigative opinions were wrong, erode our confidence in those conducting these investigations.

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Figure 12: the Lexus’ PCS images from the subject crash

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Hyundai and Kia EDR Tool Kits https://crashdatagroup.com/products/git-kia-edr-tool-kit

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I

n this article the equations necessary to calculate delta-V in three dimensions using crash test accelerometer and angular rate data are developed. This is an expansion of the development presented in reference [1] which was focused on the yaw-plane.

An expansion to three dimensions requires care be taken in the instrumentation setup of the crash test vehicle and an understanding of the effects of gravity on accelerometers that undergo general rotational motion. An additional consideration is that of describing vehicle orientation. In full-scale automobile crash testing, angular rate sensors are often used to measure test vehicle angular velocity. Typically, these sensors are oriented such that the angular rates about the vehicle longitudinal, lateral and vertical axes (p, q, and r as shown in Figure 1 [2]) are measured.

However, a set of three angular position parameters that define the orientation of a body and whose time derivatives are also uniquely p, q, and r, respectively, does not exist [3]. Accordingly, the orientation time-history of the vehicle cannot be determined through direct integration of angular rate sensor data.

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Figure 1: SAE vehicle coordinate system [image from SAE J670e, 1976].

To assist in the endeavor of establishing a vehicle orientation time-history, a set of parameters known as Euler angles are used. There are several different sets of Euler angles that one can employ for a motion analysis; the set used herein is often used in aeronautical engineering and comprises the heading angle (ψ), attitude angle (θ), and roll (or bank) angle (φ). To visualize the orientation of a body relative to a space-fixed system (X, Y, and Z axes), the rotations take place conceptually in the listed order (ψ,θ,φ). Referring to Figure 2, the first rotational displacement (heading angle ψ) is about the Z axis and establishes an x'-y'-z' orientation.

The second rotational displacement (attitude angle θ) is about the y' axis and establishes an x"-y"-z" orientation. The final rotational displacement (roll angle φ) is First rotation (T1):

about the x" axis to establish the final x-y-z vehicle orientation. The transformation matrix for each rotation is provided below. Note that “S” and “C” represent the sine and cosine, respectively, of a given angle.

Using the above transformations and the proper substitutions, the transformation from a space-fixed coordinate system to a vehicle-fixed coordinate system is then found to be:

Second rotation (T2):

Third rotation (T3):

Figure 2: Euler angle progression

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Accelerometers in Rotating Reference Frames (1) Working through the matrix multiplication, we get:

(2) The subscript “SV” indicating a Space-fixed to Vehiclefixed transformation.

In equation (2), the transformation matrix TSV is orthogonal (the resultant of each vector formed by a row or column of the matrix is equal to one, and the dot product of any of the vectors with another is zero), thus its inverse is simply the transpose of TSV. Therefore, the transformation from a vehicle-fixed orientation to the space-fixed system is expressed as:

(3) Through the use of the individual Euler transformation matrices defined above, and paying close attention to when in the sequence particular rotations are made, the vehiclefixed angular velocity components can be expressed as follows:

When evaluating accelerometer data from crash tests in which the vehicle has undergone significant pitch and roll orientation changes, the data needs to be corrected to extract the accelerations that reflect the actual motion of the vehicle. An accelerometer that is rotated to some general orientation and then held fixed will produce an output that reflects the external forces required to keep it at rest (i.e., resist gravity). The magnitude of this output is relative to the external forces acting on the accelerometer at the orientation in which it was balanced or “zeroed” (e.g., a vehicle positioned on the inclined surface of a rollover dolly). Thus, the static output vector of the rotated accelerometer can be expressed as follows:

(6)

In equation (6), the vector f is the external force per unit weight acting on the accelerometer at some general orientation, and the vector f0 is the external force per unit weight acting on the accelerometer in its initial orientation. The units for these forces are expressed as multiples of gravity (g).

Using an SAE coordinate system as depicted in Figure 3, the general expressions for the external forces acting on a rotated tri-axial accelerometer in a vehicle-based reference frame using the space-to-vehicle-fixed transformation matrix (TSV) defined above are:

(7)

(8)

(4) Equations (4) can be rearranged to provide the Euler angle rates in terms of the vehicle-fixed angular rates and the Euler angles. These Euler angle rates are expressed in equations (5).

(5)

Using data from an angular rate sensor triad or an array of accelerometers, the time-history of the Euler angles can be found through numerical integration of equations (5).

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Figure 3: Accelerometer rotated in pitch and roll

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Where the external force, expressed in the space-fixed coordinate system defined in Figure 3 is:

(9)

From equations (7) and (8), the static accelerometer output at some general orientation is then found to be: (10)

It can be seen from equation (10) that if we were to place an accelerometer on a level table, balance it with its positive z-axis pointing down then turn it upside down and position it back at rest on the table (a pitch or roll angle change of 180 degrees), the transducer output would be +2g. Similarly, if we were to roll the accelerometer +90 degrees, we’d see an output of -1g in the y-direction. A pitching of the accelerometer by +90 degrees would result in a +1g output in the x-direction. Readers who have worked with accelerometers may have observed this behavior.

(14)

To determine the time-history of the dynamic accelerations, as defined in equation (12), the time-histories of the attitude angle and the roll angle must be found, and, as mentioned previously, this is accomplished through numerical integration of equations (5). Note that unless the test vehicle is appropriately instrumented to allow for the determination of the Euler angles, the proper acceleration corrections cannot be applied.

The use of rate sensors to measure p, q, and r has already been mentioned as a convenient method to gather timehistory data for these parameters. If rate sensors are not a part of the instrumentation package for a given test, then these angular velocities can be arrived at through the use of an array accelerometers. However, as will be shown, some care must be taken when using this approach. The acceleration components from a tri-axial accelerometer located at a point P on a vehicle, assuming this point doesn’t move relative to the center of gravity (CG), can be expressed as follows: (15)

Any acceleration experienced by the accelerometer representative of actual motion will be superimposed on the static values described by equation (10). Therefore, to extract accelerations representing the dynamic motion of the vehicle, the static acceleration components expressed in equation (10) must be subtracted from the measured accelerations (am). The corrected accelerations (aD), in a vehicle-fixed reference system, are then expressed as:

where rP/CG is a vector that describes the location of point P relative to the CG (the vector direction being from the CG to point P). The components of this vector equation are:

(11)

As can be seen from these equations, the presence of squared and cross terms renders the derivation of the p, q, r angular velocity components from an array of generally-positioned triaxial accelerometers a difficult task. This endeavor can be made somewhat easier if an array of one tri axial accelerometer and three bi-axial accelerometers are positioned strategically within the test vehicle. One possible accelerometer array is shown in Figure 4, where a triaxial accelerometer set at location 1 serves as the base of an array of three other biaxial accelerometers. In this array, set 2 is at the same lateral and vertical location as set 1, set 3 is at the same longitudinal and vertical location as set 1, and set 4 is at the same longitudinal and lateral location as set 1. There are no constraints on the location of accelerometer set 1; it does not need to be located at the CG of the vehicle. The only constraint for this setup is that the accelerometer sets remain fixed relative to one another. Taking the differences of the components from these accelerometer sets results in the following equations:

The components of Equation 11 are expressed explicitly as:

(12)

The transformation of the corrected vehicle-fixed accelerometer data to a space-fixed coordinate system is carried out by multiplying equation (11) with the vehicle-tospace-fixed transformation matrix TVS as follows: (13) Since TVS is the inverse of TSV, the multiplication of these two matrices results in the identity matrix I. Equation (13) can then be restated as:

(16)

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(19) The time derivatives of the unit vectors are: (20) (17)

Carrying through the cross-products and collecting terms we get:

Equations (17) provide a convenient set of equations that can be numerically integrated to determine the angular rate time history. A 9-accelerometer array configuration is one that has been used for the determination of crash test dummy head angular accelerations [4].

(21)

Acceleration in Rotating Reference Frames The absolute velocity vector of a point on a vehicle can be described, in the vehicle-fixed reference frame (with unit vectors e1, e2, and e3 aligned with the vehicle x, y, and z axes, respectively), as the following:

(18)

The absolute acceleration of this point on the vehicle is then the time derivative of equation (18) and is developed as follows:

Here we see that to determine the vehicle-fixed velocity components requires integrating terms with coupled linear and rotational velocity terms. An alternative method to determine the vehicle-fixed velocity components is to first integrate the components of equation (14) to determine the space-fixed velocity components (VG) at a given accelerometer location. Once these are determined, the vehicle-fixed components at that location are found by using the transformation matrix TSV:

(22)

Regardless of method, once the vehicle-fixed velocity components are determined, the delta-V at the particular accelerometer location is then:

(23)

Where TDV is an Euler angle matrix that transforms the initial velocity vector VPL0 to the coordinate orientation at the time the delta-V calculation is made. This matrix is defined as: (24)

It is assumed here that the Euler angles at the initial orientation are all zero. If the initial orientation is such that there are non-zero initial values, then the values representing the change of vehicle orientation should be used in TDV. For example, if the initial roll angle is 23 degrees and a delta-v calculation is to be made at a subsequent roll angle of 90 degrees, then a value of 67 degrees would be used to make the transformation.

The delta-V components of equation (24) stated explicitly are:

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(25)

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Quite often the delta-V at the vehicle CG is of principal interest. If a triaxial accelerometer is mounted at the CG, then the calculations through equations (25) suffice. If, for a given test setup, it is more prudent to install a triaxial accelerometer at a location other than at the CG, then the velocities at the CG can be found by the following:

(26)

We can see from equations (25), that for a two-dimensional yaw plane situation (i.e., pitch and roll angle changes are negligible and considered to be zero), the x- and y-components of equations (25) reduce to the vector components of equation (21) of reference [4], and the z-component reduces to a simple difference between the pre- and postimpact vertical velocities.

At this point, we find that all the parameters necessary to determine the velocity components and delta-V components of any point on a vehicle can be found from measurements obtained through the use of one triaxial accelerometer set and one triaxial angular rate sensor. If accelerations at the CG are desired to be known for the situation where good sense dictates a non-CG installation for the triaxial accelerometer, then the test setup will need to include accelerometers installed in at least three different locations. This is recommended because equations (16) will need to be employed and numerically differentiating a sampled data set (angular rates in this case) can produce extremely noisy results. Additionally, the placement of the accelerometers require some strategy in their installation in order to make good use of equations (16) to arrive at a solution. An example of a suitable configuration is shown in Figure 5 with the active axes at each location indicated.

From the measured accelerometer and angular rate data, the three angular accelerations can be found through equations (27). Regardless of the accelerometer configuration, once the angular acceleration time-history data is determined, equations (16) can be rearranged to calculate the vehicle CG acceleration components. If there are no restrictions in available equipment, the setup may as well include three triaxial accelerometer sets, thereby gaining some redundancy.

(27)

Figure 5: Example accelerometer setup for the calculation of angular accelerations

Quite often the delta-V at the vehicle CG is of principal interest References 1. Marine M., & Werner, S., “Delta-V Analysis from Crash Test Data for Vehicles with Post-Impact Yaw Motion,” Society of Automotive Engineers, Paper No. 980219, 1998 2. Society of Automotive Engineers Surface Vehicle Recommended Practice J670, Vehicle Dynamics Terminology 3. Greenwood, D., Principles of Dynamics, 2nd Edition, Prentice-Hall, Inc., 1988 4. Chou, C., & Sinha, S., “On the Kinematics of the Head Using Linear Acceleration Methods,” Journal of Biomechanics, Vol. 9, pp607-613, 1976

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Toyota Vehicle Control History: "Sudden Braking History” Recording Characteristics Joseph Teitelman, Eric Rodos, Daniel Wolfe, & Michael Helker ARCCA Inc.

ntroduction With the recent introduction of vehicle control history (VCH), Toyota has ushered in a new frontier in advanced vehicle data which leverages the ever increasing amount of technology contained within modern vehicles. First adopted in 2013, Toyota VCH is a new data recording functionality which records and stores information relating to a variety of non-collision dependent triggers. Vehicle control history triggers include specified driver behaviors, the activations of numerous vehicle systems, and occurrences related to advanced driver assistance systems (ADAS). Toyota VCH expands upon both the range of incidents where collision investigators and reconstructions may expect stored data, and the amount of data which is stored relating to those incidents. Toyota VCH records are generally recorded over a longer period of time and at an equal or higher frequency than traditional event data recorder (EDR) data and can even include the date/time from the vehicle’s clock and photographs from the vehicle’s forward facing camera.

I

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Starting in the model year 2000, Toyota began equipping its vehicles with event data recorders, which record and store information related to qualifying collision events. Pre-crash data elements from sensors and systems located across the vehicle were introduced in Toyota EDRs shortly thereafter. Modern Toyota EDRs contain data parameters which extend beyond the data elements prescribed by 49CFR563, which Toyota considers as only minimum requirements.

[1] Previous research has been published which has focused on the accuracy of the data recorded across various generations of Toyota EDRs. [2] This research, and the general acceptance of event data throughout the accident reconstruction community has resulted in the establishment of Toyota EDR data as a critical tool to collision reconstructionists.

Much like EDR events, Toyota VCH records are recorded utilizing various sensors and vehicle systems and are stored in non-volatile memory within the airbag control module. However, it should be noted that vehicle control history is located in separate memory areas from EDR data and that the imaging procedure differs between the two data recording functionalities.

Despite the accuracy of vehicle control history data being tied to that of Toyota EDR, prior research has been conducted which specifically validates the accuracy of vehicle control history data. These studies, which evaluated various VCH data elements against reference instrumentation, have shown accuracy which is comparable to that of EDR data. [3,4] Vehicle control history data provides tremendous utility to collision reconstructionists. By storing data relating to a collection of non-collision dependent triggers, critical information is now often accessible regarding incidents that are not traditionally recorded

by EDRs including near-impacts and low-severity impacts such as those involving pedestrians, cyclists, and animals. Based on field investigations conducted by these authors, and on published literature on Toyota VCH, [5] it is clear that the “sudden braking history” trigger has a particularly high propensity to record as a result of, or in the moments preceding a collision event. For models equipped with Toyota Safety Sense 2.0 or newer, photographs from the forward facing camera are recorded and stored along with the data for each “sudden braking history” trigger. [6] The “sudden braking history” trigger shares a memory recording area with the similarly named “sudden turning history”. The prevalence of “sudden turning history”

has been shown to be much less than “sudden braking history”, however, the data is equally valuable when available. As the circumstances which result in the recording of these triggers often occur prior to a collision, the recorded data regularly overlaps with the 5 seconds of pre-crash data which is recorded by the EDR. For these reasons, this research will focus on the “sudden braking history” and “sudden turning history” triggers.

Since 2012, automobile manufactures have had regulatory guidance provided by 49CFR563 regarding event data recorder trigger thresholds. There has been considerable research conducted within the accident reconstruction community which has aimed to investigate and characterize the actual recording behavior of various event data recorders. Unlike with EDRs, there is no existing guidance or accepted benchmark at this time to follow regarding the trigger thresholds for non-collision dependent data such as Toyota VCH. Many vehicle control history events have clear triggering parameters such as any recorded activation of the antilock-brake, traction control, or stability control systems. However, the sudden braking/turning history triggers have more abstract labels which do not provide a clear understanding of their recording behaviors. In a publication by Toyota, [7] it was referenced that the parameter which triggered the recording of “sudden braking history” was Gx, or longitudinal acceleration, however, a specific threshold was not mentioned and an example dataset was not provided. In said publication, no mention of the “sudden turning history” trigger was made.

It is critical to the accident reconstruction community to understand when, and why electronic vehicle data is recorded. This research aims to investigate which parameters trigger the recording of the “sudden braking history” and “sudden turning history” records within Toyota vehicle control history, and to identify their trigger thresholds. Additionally, the collected data will be analyzed and recording characteristics and trends which appear within the information will be explored.

Methodology For this study, vehicle control history data was downloaded from 19 Toyota/Lexus vehicles in order to acquire and analyze naturalistic occurrences of the “sudden braking history” and “sudden turning history” triggers. The involved vehicles, detailed in Table 1, were comprised of 10 different models including se-

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dans, SUVs, and pickup trucks which covered the model years 2015-2021. Each vehicle was downloaded either during field investigations which followed a collision event, or as part of ongoing research into Toyota VCH recording characteristics. All data which was downloaded during field investigations was scrutinized, and sudden braking/turning history triggers which were found to be recorded during a severe collision were removed from the study. The remaining events were deemed as naturally occurring and are representative of driver/vehicle behaviors which either precede a collision event, or are unrelated to one.

All data was downloaded using a licensed version of Toyota Techstream diagnostics software in conjunction with standard imaging practices. [8,9] The collected data was primarily downloaded by connecting to the vehicles’ onboard diagnostics ports with either the Nexiq USB-Link 2 or Bosch CDR900 interfaces. On several occasions, data was imaged by connecting directly to an airbag control module. This was accomplished using a cable which was built in-house utilizing specifications from Crash Data Specialists. The two imaging methods, depicted in Figures 1 and 2, were conducted sequentially in a single vehicle, demonstrating that identical VCH data is retrieved with either method, with the exception of the date/time and photographs which are stored outside of the airbag control module and are therefore not available during directto-module imaging.

The memory recording area shared by the “sudden braking history” and “sudden turning history” records can contain

Table 1: List of imaged vehicles

Table 2: List of recorded data elements for the “sudden braking history” and “sudden turning history” records (* denotes parameters which vary based on year/make/model)

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Figure 1: Toyota vehicle control history imaging utilizing on-board diagnostics port

Figure 2: Toyota vehicle control history imaging utilizing direct-to-module cable

a maximum of 4 total events. Each sudden braking/turning history trigger records 41 to 44 data elements, dependent on model and production year. A list of recorded parameters can be found in Table 2. The data is recorded over a 10.2 second period which is centered at the trigger and is recorded at 6.67 Hz (every 150 msec). The trigger itself is recorded as a separate datum, which can occur in-between two regularly spaced data points, resulting in a total 70 data points stored for each record.

just above the observed trigger threshold. Toyota VCH data was imaged throughout the testing to demonstrate which tests triggered the “sudden braking history” event.

During each download, the imaged data was saved in its native format “.TSE” (Toyota Service Event), which can subsequently be opened using Toyota Techstream software to view downloaded VCH data in both tabular and graphical form. Additionally, “.CSV” (Comma-Separated Values) files were generated from each download for use in data analysis. Within the “.CSV” files, the stored data was sorted by trigger type and important parameters were highlighted for analysis.

In addition to the analysis of naturally occurring VCH records, instrumented testing was conducted to further investigate “sudden braking history” recording characteristics. The test vehicle was a 2020 Toyota Corolla, and the testing was conducted on an asphalt parking lot in Penns Park, Pennsylvania. Reference speed and acceleration data was obtained using both a Racelogic VBOX Mini and an Endevco model 7267A triaxial Accelerometer. Additionally, the live data recording functionality available within the Toyota Techstream software was utilized to monitor each test and record the accelerations sensed by the vehicle.

The tests consisted of controlled braking maneuvers with varying deceleration behavior. The aim of the testing was to reach peak deceleration values that were just below, and

Results/Discussion Of the data collected from the 19 vehicles involved in this study, 73 qualifying “sudden braking history” events were investigated. It was hypothesized by these authors that the triggering of the “sudden braking history” record was related to one of several recorded data elements including: vehicle speed, brake switch, brake oil pressure, longitudinal G sensor signal, and anti-lock brake activation. Each selected parameter was analyzed independently across the collected dataset to expose trends and commonalities relating to the occurrence of the trigger command. Examination of the data clearly demonstrated that the recording of the “sudden braking history” event is triggered solely by the longitudinal G sensor signal, with no identifiable relationship to any other recorded data element. This finding is consistent with previous literature from Toyota on vehicle control history.

There were 15 qualifying “sudden turning history” records within the collected data. Recorded data elements selected for investigation included: steering angle, yaw rate, lateral G sensor signal, EPS torque sensor, and stability control activation. Similar to the findings related to “sudden braking history”, the analysis established that recording of the “sudden turning history” event is triggered by the lateral G sensor signal. Trends in the collected data were observed relating to several other recorded parameters; however, these data elements had no causative relationship with the trigger command.

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Figure 3: Live acceleration and speed data from test which did not trigger a “sudden braking history” record

Figure 4: Live acceleration and speed data from test which did trigger a “sudden braking history” record (annotation denotes the timing of the VCH trigger)

In addition to defining the triggering parameters of longitudinal and lateral acceleration, analysis of the collected data identified the “sudden braking history” and “sudden turning history” recording thresholds. Toyota VCH acceleration data has a recording range of ±8.9725 m/s2 with a reporting resolution of 0.07178 m/s2. None of the analyzed events contained a peak acceleration value below ±0.7 g. Within the collected data, the lowest longitudinal acceleration which resulted in the recording of “sudden braking history” was -6.96266 m/s2 (-0.71 g), and the lowest lateral acceleration which resulted in the recording of “sudden turning history” event was -6.89088 m/s2 (-0.70 g). Therefore, the analyzed events demonstrate that the minimum recorded acceleration value which results in the triggering of a sudden braking/turning history record is the first reporting interval which exceeds the 0.7 g threshold.

resolution, recording frequency, and recording priority, the occurrence of the recording trigger often comes after the first datum which crosses the acceleration threshold. The trigger delay, demonstrated in Figure 5, rarely exceeds 100 msec, meaning that it occurs prior to the next recorded data point and therefore does not alter the recording window.

A series of instrumented tests further established a recording threshold for “sudden braking history” of 0.7 g. Figures 3 and 4 present longitudinal acceleration and vehicle speed data from two tests which were collected using the Toyota Techstream live data recording functionality. The first test, depicted in Figure 3, reached a peak deceleration of approximately -0.68 g and did not result in the recording of a vehicle control history record. The second test, depicted in Figure 4, crossed the 0.7 g threshold and triggered the recording of a vehicle control history record. All conducted tests followed the recording behavior observed in the analysis of the naturalistic data.

In both the naturalistic and controlled test data, a recording latency was observed throughout the analyzed events.

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During the analysis of the collected vehicle control history records, a data irregularity was identified. An initial review of the 2018 Toyota Tundra data showed that the polarity of the longitudinal and lateral g sensor signals were reversed.

This was made evident when comparing the recorded acceleration values against other data elements such as brake switch, vehicle speed, steering signal, and yaw rate. Upon further investigation, it was concluded that the polarity of the longitudinal and lateral G sensor signals was reversed only within the generated “.CSV” file, and that the correct polarity was displayed when viewing the data in the “.TSE” format. This abnormality proved true across all recorded VCH records from the vehicle and was repeated when additional “.CSV” files were generated from the Techstream software. It is unclear if this was an anomalous occurrence, or if there is a consistent issue which impacts data from a range of vehicles.

It was clear from the analysis of the “sudden turning history” records that the recording threshold was the same in both lateral directions, however, the longitudinal accelerations recorded in relation to the analyzed “sudden braking history” records were predominantly in the negative (front-to-back) direction. Out of the collected data, a single naturally occurring “sudden braking history” record

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Figure 5: Recorded VCH acceleration data from a “sudden braking history” record (selection of data emphasized to demonstrate trigger latency) was triggered by a positive (rear-to-front) longitudinal acceleration value. The crossing of the +0.7 g threshold was the result of aggressive driver inputs and was not accompanied by any brake application. It was concluded that the acceleration data for this record was reported with the correct polarity, and that the recording of the event indicates that the 0.7 g longitudinal acceleration threshold is true in both directions for the “sudden braking history” trigger.

As multiple vehicle control history records are often recorded in rapid succession in the moments preceding a collision, the frequency of which additional related triggers occur in relation to “sudden braking history” was investigated. Data elements relating to the pre-collision system (PCS) are not recorded in “sudden braking history” records. However, it was observed in the VCH data of vehicles equipped with the pre-collision system that PCSrelated events can be triggered along with “sudden braking history” prior to a frontal collision. Anti-lock brake system (ABS) activation was recorded during 27 of the 73 naturally occurring “sudden braking history” events. In all but one of these instances, ABS engagement occurred subsequent to the crossing of the 0.7 g recording threshold, meaning that the recorded data window for the “ABS operation history” event would slightly extend the timeframe in which continuous data was stored relating to the event.

In the same way, when a “sudden braking history” or “sudden turning history” event is recorded prior to a collision which triggers the recording of EDR data, overlapping data is stored from the two sources over a period of time

which often exceeds the approximately 5 second long window recorded by EDR alone.

The discovery of a 0.7 g recording threshold for both longitudinal and lateral acceleration further establishes the importance of Toyota VCH data to collision reconstructionists. As has been identified in previous research, [10] a recent shift in the recording behavior of Toyota airbag control modules limits the amount of low-severity collisions which qualify as a non-deployment event in the EDR algorithm. This has created a void of useful data relating to certain types of collision events which can now be filled by downloading vehicle control history. In addition, vehicle control history data can be utilized along with an EDR download in certain circumstances.

Conclusions The goal of this research was to investigate the recording characteristics of two Toyota vehicle control history triggers. Based on an evaluation of naturally occurring VCH events, it was determined that the triggering parameter for the “sudden braking history” record is longitudinal acceleration and that the triggering parameter for the “sudden turning history” record is lateral acceleration. Analysis of the collected data identified a triggering threshold of ±0.7 g for both records. The lowest VCH acceleration reporting interval which crosses this threshold is ±6.89088 m/s2. The recording behavior exhibited by the naturally occurring data was further validated by instrumented testing.

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"Toyota vehicle control history has been shown to have utility in investigating a wide range of collision events" It was identified that the “sudden braking history” event regularly is recorded in conjunction with other independently triggered VCH records. The most common VCH event which accompanied “sudden braking history” in the analyzed data was “ABS operation history”. The combination of multiple VCH records which are triggered in rapid succession can allow for a greater overall window of recorded data in relation to an investigated collision. Furthermore, the ability to download Toyota VCH in addition to EDR data can potentially provide a collision reconstructionist with multiple sources of electronic vehicle data.

for download. Acknowledgment is given to Jeremy Robbins for his assistance with the conducted testing.

References 1. United States Code of Federal Regulations, 49 CFR Part 563 “EVENT DATA RECORDERS” 2. Richard R Ruth; Accuracy of Toyota Event Data Recorders, Collision – The International Compendium for Crash Research, Volume 7, Issue 1 3. Robert D Anderson and Wesley Vandiver; Testing and Validation of Toyota Vehicle Control History Speed and Acceleration Data, Collision – The International Compendium for Crash Research, Volume 14, Issue 2 4. Peter Xing, Mike Yang, Brandon Tsuge, Thomas Flynn, Jonathan Lawrence, and Gunter P. Siegmund (2018) The Accuracy of Toyota Vehicle Control History Data during Autonomous Emergency Braking.

SAE Technical Paper No. 2018-01-1441 5. David M Hallman, Robert D Anderson, and Billy S Cox Jr; Toyota Vehicle Control History Case Studies, Collision – The International Compendium for Crash Research, Volume 14, Issue 2 6. Wesley Vandiver and Robert Anderson; Toyota Vehicle Control History – The Basics of What You Need to Know, Crashology: The Science of Crashes, Collision – The International Compendium for Crash Research, Volume 14, Issue 2 7. Motomi Iyoda, Tom Trisdale, Rini Sherony, Daniel Mikat, and William Rose (2016) Event Data Recorder (EDR) Developed by Toyota Motor Corporation.

SAE Technical Paper No. 2016-01-1495

Toyota vehicle control history has been shown to have util- 8. Toyota Techstream software is available for purchase at techinfo.toyota.com ity in investigating a wide range of collision events. The establishment of an acceleration-based recording threshold 9. Alan Moore; A How-To Guide For Downloading Toyfor the “sudden braking history” and “sudden turning hisota’s Vehicle Control History Data, Collision – The tory” events allows for collision reconstructionists to preInternational Compendium for Crash Research, Voldict when such data will be available. The relatively low acume 14, Issue 1 celeration required to trigger the recording of the “sudden braking history” and “sudden turning history” events en- 10. Peter Xing, Felix Lee, Thomas Flynn, Craig Wilkinables access to valuable electronic vehicle data as a result of son, and Gunter Siegmund (2016) Comparison of a collision which fails to reach the EDR non-deployment the Accuracy and Sensitivity of Generation 1, 2 and recording criteria.

3 Toyota Event Data Recorders in Low-Speed Collisions. SAE Technical Paper No. 2016-01-1494 Acknowledgements The authors would like to thank the colleagues, friends, and family members whose vehicles were made available

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Electric Scooter Hard Brake, Acceleration and Top Speed Testing Robert D Anderson Biomechanics Analysis

Michael Rosenfield RSR Engineering

Russell Anderson

Collision Analysis & Research

I

ntroduction Recently, the popularity of e-scooters has mushroomed. These electric vehicles are inexpensive, fun to ride, they may not require a license or insurance and they are thought to be an eco-friendly choice. Sharable e-scooters can be rented with an app, then left at the destination for the next customer that comes along. Many cities have started pilot programs to test the viability of e-scooters as a mobility option.[1,2,3,4] They are popular around college campuses, beaches, urban areas, etc. With their rise in popularity, there has been a commensurate rise in crashes and injuries.[5,6,7,8,9]

suggested although it was acknowledged that a 2017-18 UCLA study found that most e-scooter riders did not wear a helmet and that 40 percent of the injuries observed in the study were head injuries.[7] BBC reports that while escooters offer a seemingly fun and environmentally friendly option for short journeys, a range of questions about their safety and sustainability have emerged in the previous two years. Although shared models are emission free at the point of use, manufacturing, moving and redistributing them results in greenhouse emissions. On pathways and sidewalks, e-scooters pose dangers to pedestrians and wheelchair users, and particularly to people with visual impairment, but using scooters on roads without sufficient infrastructure such as cycle lanes is also risky. [8] Todd et al. conducted an observational study of e-scooter rider behavior in west Los Angeles. It was noted that there was a potential for e-scooter operators to accelerate to the 15 mph maximum speed allowable in California sooner than a bicycle or traditional kick scooter. It was found that the proportion of helmet users on e-scooters was much less than that of bi-

Consumer Reports found that most of the e-scooters they tested had a top speed of 15 mph on flat pavement. It was concluded that if used safely, e-scooters can be a viable - not to mention quick, portable, fun - way to fill transportation gaps. Helmet and possibly pads and guards were

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Figure 1: Still frame from SATAI 2020 Number 1 Conference Crash Video cyclists. In fact, nearly 90 percent of e-scooter riders did not wear a helmet. Operators were demonstrated to be more relaxed about complying with vehicle codes as demonstrated by the greater likelihood of travel in the opposite direction of traffic and on sidewalks. [10]

their testing, compared to riders in the 115 to 150 pound range, lower acceleration and braking performance was observed for a 274 pound rider. It was reported that the share application might have a ride history or summary for the rental, although this had not yet been explored.

Scooter crashes have also become part of crash test demonstrations. For example, at the 2020 SATAI Crash Conference, the crash program led with two automobile-toscooter crash demonstrations, one of which is shown in Figure 1. During the conference, Accident Science, Inc. presented the results of yet-to-be-published instrumented e-scooter acceleration and hard brake testing. [11] In

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Figure 2: Lime vs. Bird Figure 2 shows a comparison between Lime and Bird escooter share service. However, a comprehensive study of e-scooter performance may not be required to characterize performance since Lime, Bird and Spin all ultimately rely on Ninebot, a Chinese scooter company that has merged with Segway. [12] Top speed, time to various levels of speed and 15 mph stopping distance are available for 123 models of 33 manufacturers from Electric Scooter Guide and have been compiled by Bartlett and Craig. [13] In the present study e-scooter speed versus distance, speed, distance and acceleration versus time, as well as hard brake, 80 Collision Magazine - Volume 15 Issue 2

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and top speed test results are presented to provide an improved basis for crash reconstruction analysis.

Instrumentation and Methods In this study, two share e-scooters and two privately owned e-scooters were tested. Tests runs consisted of maximum acceleration followed by hard braking to a stop. The testing of Bird e-scooter (Figure 4) and Razor e-scooter (Figure 9) was conducted at the Tempe Town Lake, Tempe, AZ, in April 2019. A privately owned Glion e-scooter (Figure 14) was tested in January 2019 and a privately owned Swag-

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speed and acceleration data was exported in CSV format so that it could be imported into Microsoft Excel and HydeSoft Computing’s DPlot for processing and graphing.

Test Results The Bird E-scooter (Figure 4) is rear wheel drive, rear wheel braked and the rider must kickoff to start movement. The test riders ranged in weights from 125 to 200 pounds.

Hard braking of the Bird with rear wheel skidding produced essentially constant decelerations. The average decelerations were within the 0.24 to 0.33 g range. The level of deceleration was found to be rider dependent. Riders that deliberately shifted their weight backwards were able to achieve average decelerations of 0.3 g’s or higher while riders that maintained their upright posture tended to have average decelerations of about 0.25 g’s.

Figure 3: Racelogic VBOX Sport tron e-scooter (Figure 19) was tested in November 2019.

The privately owned e-scooters testing was conducted in Seaside, OR. Speed and acceleration data was obtained from Racelogic VBOX Sport (VBS) shown in Figure 3. The VBS records data at 20Hz and it has an internal battery and an internal GPS antennae with a ground plane. Speed is calculated from the Doppler shift of GPS radio signals, while distance and acceleration is calculated from the speed. The Racelogic reported accuracy for acceleration is ±1% or better, distance traveled ± 0.05%, and speed ± 0.13 mph or better, when smoothed with a moving average of 4 samples. [14] The VBOX data was visualized and processed using the latest version of Racelogic’s VBOX Test Suite. UTC Time,

Figure 4: The Bird e-scooter

The measured speed versus distance, speed versus time, distance versus time and acceleration versus time relationships for the Bird e-scooter are shown in Figures 5 through 8.

The Razor e-scooter (Figure 9) is rear wheel drive and rear wheel braked. It can be ridden while seated or standing on the platform, and movement can be started without kicking off. The test riders ranged in weights from 125 to 200 pounds.

"Hard braking of the Bird with rear wheel skidding produced essentially constant decelerations"

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Figure 5: Speed vs. Distance for the Bird e-scooter

Figure 6: Speed vs. Time for the Bird e-scooter For the Razor, hard braking produced essentially constant decelerations. The average decelerations were generally within the 0.25 to 0.36 g range. Riders that deliberately shifted their weight backwards were able to achieve average decelerations of 0.3 g’s or higher while riders that maintained their upright posture tended to have average decelerations of within the approximate 0.25 to 0.3 g range.

The measured speed versus distance, speed versus time, distance versus time and acceleration versus time relationships for the Razor e-scooter are shown in Figures 10 through 13.

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The Glion e-scooter (Figure 14) is rear wheel drive, rear wheel braked and movement is initiated by the rider kicking off. The braking mechanism is apparently achieved through the electric motor, perhaps regenerative, without any evidence of a mechanical brake. The test riders ranged in weights from 155 to 290 pounds.

Hard braking of the Glion did not produce any skidding.

Maximal braking produced essentially constant decelerations. The average decelerations were within the 0.1 to 0.18 g range.

The measured speed versus distance, speed versus time, distance versus time and acceleration versus time relation-

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Figure 7: Distance vs. Time for the Bird e-scooter

Figure 8: Acceleration vs. Time of the Bird e-scooter

"Hard braking of the Glion did not produce any skidding.

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ships for the Glion e-scooter are shown in Figures 15 through 20.

The Swagtron e-scooter (Figure 21) is front wheel drive and front wheel braked. Like the Razor, movement could be initiated with little or no kickoff. The test riders ranged in weights from 155 to 290 pounds.

Figure 9: the Razor e-scooter with VBOX Sport installed

Hard braking of the Swagtron did not produce any skidding. Maximal braking produced essentially constant decelerations. The average decelerations were within the 0.08 to 0.12 g range.

Figure 10: Speed vs. Distance for the Razor e-scooter

Figure 11: Speed vs. Time for the Razor e-scooter 84 Collision Magazine - Volume 15 Issue 2

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The measured speed versus distance, speed versus time, distance versus time and acceleration versus time relationships for the Swagtron are shown in Figures 5 through 8.

Discussion

The performance measured for electric scooters with riders in the 125 to 290 pound range is presented here in an effort to expand the available body of information available to crash investigators and reconstructionists regarding this emerging group of vehicles.

The electric scooter or e-scooter has been around since 1920 (Figure 24). However, with recent improvements in battery technology and sharing programs, their popularity has risen sharply in recent years. The need to characterize the performance of this emerging class of vehicles has been necessitated by the increased number of crashes and injuries associated with e-scooters.

Testing demonstrated that maximal braking and/or skidding of the e-scooters resulted in constant decelerations.

As such, it is appropriate to use constant decelerations relationships for analysis of hard braking-skidding of escooters. However, as depicted in the above Figures, maximal acceleration produced speeds, distance traveled and accelerations that all varied with time and/or distance.

Figure 12: Distance vs. Time for the Razor e-scooter

Figure 13: Acceleration vs. Time of the Razor e-scooter www.collisionmagazine.com

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Therefore, unless the e-scooter was stationary or traveling at a constant speed, it is desirable to account for the non-constant speed, distance traveled and acceleration in the analysis of an e-scooter crash or other event.

Figure 14: A Glion e-scooter

The privately owned Swagtron and Glion are not capable of skidding and their average decelerations during hard braking were within the 0.08 to 0.18 g range. Figures 15 and 22 show that the top speed of the privately owned e-scooters was on the order of 10 or 11 mph. As such, their low deceleration during braking is offset by their lower top speed.

As seen in Figures 5 and 10, the test results for the shared e-scooters are consistent with their top speed being set at 15 mph.

Figure 15: Speed vs. Distance for the Glion e-scooter

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The shared e-scooters demonstrated the capability of producing rear wheel skids. Hard braking and/or skidding with the rider maintaining an upright posture resulted in average decelerations of about 0.25 to 0.3 g’s. However, for these skidding capable e-scooters, riders who shifted their weight backwards were able to achieve additional slowing with decelerations on the order of 0.35 g’s. Leaning back places more weight over the braking rear wheel so that a greater frictional force and therefore slowing can be realized.

22, 23 and 24, increased rider weight was also generally associated with lower speeds and accelerations.

For those riders who weighed more, some degradation in braking capability was observed, which is consistent with Accident Science, Inc.’s results. As shown in Figures 16,

The competing factors of rider weight and kickoff effort are demonstrated in Figure 16, where the 220-pound rider’s kickoff produced a higher initial speed, but the 155 pound

Interestingly, the dominant factor for the initial speed and acceleration is the rider’s kickoff. This is demonstrated in Figures 18 and 20, where the 220-pound rider consistently had a more vigorous kickoff, that the other riders. On the other end of the spectrum, the 180-pound rider’s kick off appeared to be consistently just sufficient to initiate movement and engage the electric motor.

Figure 17: Speed vs. Time for the Glion e-scooter

Figure 18: Speed vs. Time for two of the Glion e-scooter riders www.collisionmagazine.com

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Figure 19: Distance vs. Time for the Glion e-scooter

Figure 20: Acceleration vs. Time average for each Glion e-scooter rider rider ultimately achieved a higher speed after 30 feet. A similar pattern is observed between the 290- and 180-pound riders also shown in Figure 16.

Summary and Conclusions In this study, differences in top speed, acceleration and braking performance were observed between the shared and privately owned e-scooters. The variation in performance between the two shared scooters and the two privately owned scooters was less distinct. It is not known if these similarities and differences are generally the case or specific to the models used in this study.

In analyzing a crash, using the demonstrated performance of the crash scooter or the same make and model as the crash scooter with a rider of comparable weight would be expected to result in the best representation of that crash scooter’s performance.

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Figure 22: Speed vs. Distance for the Swagtron e-scooter

Figure 23: Speed vs. Time for the Swagtron e-scooter kickoff while accelerating from a stop, and rider posture during braking. With a range of braking postures and kickoff behavior, the results of this study can serve as a guide for the amount of variation that could result. Although not part of this study, it would seem reasonable to expect that the grade of the scooter’s path should also be accounted for.

References 1. “City of Phoenix, E-Scooter Pilot https:www.phoenix.gov/streets/scooters

Program,”

2. “City of Tucson E-Scooter Pilot Program,” https:www. tucsonaz.gov/bicycle/e-scooter-pilot-program 3. “Portland Bureau of Transportation, Shared Electric Scooter Pilot,” https://www.portlandoregon.gov/transportation/77294

Figure 26: Electric Scooter, circa 1920 [15] 4. “E-Scooter Firms get the Green Light to Start Trials of Up to One Year on UK Streets,” TechCrunch, June 30, 2020.

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Figure 24: Distance vs. Time for the Swagtron e-scooter

Figure 25: Acceleration vs. Time of the Swagtron e-scooter 5. “Boom in Electric Scooters Leads to More Injuries, Fatalities,” Accident Reconstruction Journal, Vol 29, No. 4, July/August 2019.

6. “When Electric Scooters Crash, Who Pays The Bills?”

Accident Reconstruction Journal, Vol 29, No. 4, July/ August 2019.

7. “Electric Scooter Reviews, E-Scooter Tests,” Consumer Reports, March 19, 2020.

8. “Why we have a love-hate relationship with electric scooters,” BBC, Future Planet – Sustainability, June 9, 2020 9. Trivedi T, Liu C, Antonio A, Wheaton N, Kreager V, Yap A, Schriger “D & Elmore J, “Injuries Associated with Standing Electric Scooter Use,” JAMA Network Open. 2019; 2(1):e187381. doi:10.1001/jamanetworkopen.2018.7381, January 25, 2019.

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10. Todd, J., Krauss, D., Zimmermann, J., and Dunning, A. “Behavior of Electric Scooter Operators in Naturalistic Environments,” SAE Technical Paper 2019-011007, 2019, doi:10.4271/2019-01-1007.

11. “Electric Scooters,” presented at Southwestern Association of Technical Accident Invesitgators, January 2020, by Tim Reust, Jim Morgan and Justin Reust of Accident Science, Inc. 12. “Lime partners with Segway to Build Electric Scooters,” TechCrunch, May 1, 2018.

13. Bartlett W & Craig V, “Electric Scooters Specifications and Test Results” Accident Reconstruction Journal, Vol 31, No. 1, January/February 2021.

14. Racelogic VBOX Sport data sheet, 4/2/14.

15. “The Electric Scooter,” The Journal of the Society of Automotive Engineers, January 1920.

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CA SE SO P LU RO TI BL ON EM :

"The effect of nonplanar acceleration captured at the Airbag Control Module on the recorded EDR delta-V data"

W. R. Rusty Haight

Collision Safety Institute

David W. Sersen New York State Police

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To review…

The police are chasing a Buick LaCrosse. As pointed out, the why and where isn't terribly important to the case problem, in fact, for the case problem at hand, that it was a pursuit isn't all that critical except that the other involved vehicle is a police Chevy Tahoe. At a point just before the crash, the deputy driving the Tahoe is aware that the pursuit of the LaCrosse is coming at him, heading his way. He approaches an intersection where he knows the fleeing LaCrosse could make what would be, for the fleeing driver, a left turn or he could go straight, continuing opposite the direction the Tahoe would then have just been ap-

proaching that intersection. Frame captures from a nearby gas station security camera are offered in the Case Problem statement found earlier in this issue as well as photos of the scene and involved vehicles and applicable portions of the EDR data translation reports created with the latest version of the Bosch CDR Tool software at the time this issue of Collision is published.

The deputy in the Tahoe pulls up to the intersection slowing as the pursuit approaches in the distance. In the Tahoe's Pre-Crash Data table, we see that, during the last 5 seconds leading up to the crash, the deputy slows through 7mph (12 km/h) to a stop; his speed is very clearly 0 mph from about the -3.0 sec Pre-Crash Data sample to the -0.5 sec sample: the being last pre-crash sample before "algorithm enable."

After the Tahoe has been fully stopped for about 3 seconds, there's a pretty spectacular crash. For reasons beyond the scope of this case problem, the fleeing driver elected to drive the LaCrosse over the centerline in the road and engage the Tahoe fully head on; "hood ornament-to-hood ornament" as it were. Although the cars were actually weighed after the crash, without occupants and not including parts which were no longer with the vehicles, given that the published weights varied so much from that found for the subject vehicles we have adopted – for this Case Problem – the average weights for the involved vehicles. For the Case Problem, the weight range for the LaCrosse as previously describes was 3550 to 3835 lbs (16101739 kg), the applied average of the high- and low-end weights for the solution discussion is adopted as 3693lbs (1675 kg). For the Case Problem, the weight range for the Tahoe as previously described was 5550 to 5683 lbs (2517-2577 kg), the applied average for the solution discussion is adopted as 5608 lbs (2544 kg). The reader is welcome to adjust the weights as they feel is appropriate.

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The Case Problem underlying question really comes down to: "Did you find any errors in the data as you read through the data?" Second to that is, when it comes to computer technology and this technology in particular, "what is an 'error'?"

Solutions We should start this off by pointing out that there are a probably a number of ways one might approach analyzing or evaluating the crash, but this is being used as a Case Problem and the idea is to have the reader engage with the facts as offered against the EDR data as accessed and retrieved using the Bosch CDR tool. The goal is to point out that, when there's something that doesn't "look right" in the data, it's up to the Analyst end user to evaluate what's going on and see if there's an "error" or something else which might have an impact on the EDR data as presented. In other words, this solution isn’t necessarily the only way to evaluate a crash like this, but the crash isn't the real subject of the underlying Case Problem as much as a different look at the EDR data is. The second caveat would be that equations and methods shown here were selected from those and other potential methods, equations, or models.

The end user has a fact set in front of them and are welcome to apply any other method they choose keeping in mind the ultimate goal here is to be able to address specific "concerns" with the EDR data not to debate the application of, for example, a choice to use "weight" in pounds versus "mass" in kilograms.

By now, unless the reader just skipped to this solution, one might have looked at the EDR data as represented in the Case Problem statement and noticed that (1) the LaCrosse is – if we are to adopt that Buick's Pre-Crash data – going pretty fast at impact but (2) there's something not quite right with respect to the delta-Vs for the two vehicles.

That, of course, bring us to the first question: 1. Think about the concept of force balance. Given the average weights of the two vehicles as a starting place for your analysis, and the EDR data translation reported delta-V for the LaCrosse, what would the delta-V of the Tahoe have been in this crash?

This question, and other questions here, first requires the reader to select the applicable delta-V for each vehicle from the data as presented. From the pages of the data translation report provided, there are effectively three potential sources for this information.

The first might be found in the data table "System Status at Event (Event Record 1)" where we see the data el-

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ement "Maximum Delta-V, Longitudinal (Maximum Longitudinal SDM Recorded Vehicle Velocity Change for FSR1 Event) MPH [km/h]" and the translated value is "-48 [-77]."2 That element is coupled with the element, in the same data table: "Time, Maximum Delta-V (Time From FSR Time Zero to Maximum Longitudinal SDM Recorded Vehicle Velocity Change)(msec)" and the translated value there of "138 (ms)." What this says is that the airbag control module (ACM) captured acceleration and then calculated the delta-V out beyond 138 ms, however, at 138 ms, the ACM found the Maximum delta-V which was recorded as -77 km/h (-48 mph) rounded to the nearest 1 km/h for the resolution called out in 49CFR563.3 Another source for the LaCrosse's maximum longitudinal delta-V might be the "Longitudinal Crash Pulse (Event Record 1)" delta-V data table and accompanying graph.

This should be compared to the data found in the "Longitudinal Crash Pulse (Event Record 1)" acceleration data table and accompanying graph (the acceleration data then might be considered the third possible method of finding the delta-V for the LaCrosse).

Why so many references to the delta-V? First, the data element "Maximum Delta-V, Longitudinal" is specified in 49CFR5634 as a single maximum value for the specified axis. There, the regulation calls for the reporting of the "Maximum delta-V, longitudinal…" (which) "…means the maximum value of the cumulative change in velocity, as recorded by the EDR, of the vehicle along the longitudinal axis, starting from crash time zero and ending at 0.3 seconds. …" One should note that this identifies the maximum delta-V for the axis (and there is a similar definition for the lateral axis).

In comparison, the "Longitudinal Crash Pulse" data table and accompanying graph have a different definition. That definition reads: "…Delta-V, longitudinal means the cumulative change in velocity, as recorded by the EDR of the vehicle, along the longitudinal axis, starting from crash time zero and ending at 0.25 seconds, recorded every 0.01 seconds. …" Note, there's no mention of the maximum in that definition. The maximum delta-V is addressed by the previous definition. Nonetheless, there are some who would incorrectly conflate that definition from the regulation with the definition for the "end of event time" – another wholly separate element allowing for a reporting period to be adopted by the manufacturers and still comply with the regulation – and then try to make an adjusted "finding" of the crash delta-V using the data table (and/ or graph) which displays cumulative values, following the regulation's specifications, at 0.01 s intervals. The value for the maximum delta-V is cumulatively calculated and re-

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ported at a higher time resolution and to a specific point5 in time.

The "Crash Pulse" data table and graph aren't going to give us the specific maximum we're looking for. The Crash Pulse acceleration table might be, as previously noted, thought of as useful in terms of calculating the delta-V, however, the sample rate in that table is every 2 ms whereas the captured acceleration from which the cumulative and maximum delta-Vs are captured then recorded in the ACM is at a higher resolution/sample rate.

So, for this question (and the next one for the same reasons) one should use the "Maximum Delta-V, Longitudinal (Maximum Longitudinal SDM Recorded Vehicle Velocity Change for FSR Event) MPH [km/h]" and the translated value "-48 [-77] as found in the "System Status at Event (Event Record 1)" data table.

If we adopt that, use the average the weights of the two involved vehicles as provided and the reported delta-V of the LaCrosse – that reported as the "Maximum Delta-V, Longitudinal" – and then apply that to Equation 1, we will find that the delta-V for the Tahoe should be about 31-32 mph (49.8-51.5 km/h).

Nonetheless, if one were to calculate the delta-V using the acceleration at the time intervals reported and compare the results at the 0.01s intervals as called for in 49CFR563, they would find the delta-V as seen in Table 1. While "close," they don't match "exactly;" nor should they. The Equation 1 ACM is capturing and calculating the delta-V at a higher sample rate than we see in the acceleration data table and, The "Maximum Delta-V, Longitudinal" for the Tahoe is even where the calculation is to km/h before rounding to reported as -48 mph (-77 km/h) – coincidentally the same Imperial for display, the apparent difference is on the order as that recorded for the LaCrosse – but, given the differof 2-3 km/h – very close but not "exactly" a match. ence in their weights, one of those values can't be right. Mathematically, yes, Reported Calculated the heavier Tahoe should have the lower delta-V, however, in the data translation Time Delta-V, Delta-V, Delta-V, Delta-V, report for the Tahoe, the delta-V is re(msec) Longitudinal Longitudinal Longitudinal Longitudinal ported as much higher than 31-32 mph (MPH) (km/h) (MPH) (km/h) (49.8-51.5 km/h). That begs the ques10 -3.7 -6 -3.6 -5.8 tion then: is the delta-V reported by the 20 -9.3 -15 -8.9 -14.4 Tahoe "in error? " …or is it the delta-V 30 -16.2 -26 -15.7 -25.2 for the LaCrosse? And, of course, that 40 -21.1 -34 -20.6 -33.1 brings us back to: 50 -30.4 -49 -29.4 -47.3 "Did you find any errors in the data as 60 -39.8 -64 -38.6 -62.2 you read through the data?"

70 -42.3 -68 -41.2 -66.2 2.

Think about force balance here 80 -43.5 -70 -42.5 -68.3 again. Given the average weights of the 90 -44.7 -72 -43.4 -69.8 two vehicles as a starting place for your 100 -46.0 -74 -44.7 -71.9 analysis, and the EDR data translation reported delta-V for the Tahoe, what 110 -46.6 -75 -45.3 -72.9 would the delta-V of the Lacrosse have 120 -47.2 -76 -45.7 -73.6 been in this crash?

130 -47.2 -76 -45.9 -73.9 The same discussion found for question 140 -47.8 -77 -46.2 -74.3 #1 above applies: "which" delta-V found 150 -47.2 -76 -45.7 -73.6 in the report should we use? For the same 160 -46.6 -75 -45.3 -72.9 reasons, the answer would be that shown 170 -46.6 -75 -45.2 -72.7 for the data element "Maximum Delta-V, Longitudinal." Looking then to the data 180 -46.6 -75 -45.2 -72.8 table found in the Tahoe data labeled 190 -46.6 -75 -45.3 -72.9 "System Status at Event (Event Record 200 -46.6 -75 -45.3 -72.8 1)" we see the data element "Maximum Delta-V, Longitudinal (Maximum LonTable 1: delta-V data fore the LaCrosse www.collisionmagazine.com

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gitudinal SDM Recorded Vehicle Velocity Change for FSR Event) MPH [km/h]" and the translated value is shown as "-48 [-77]. That element is coupled with the element, in the same data table: "Time, Maximum Delta-V (Time From FSR Time Zero to Maximum Longitudinal SDM Recorded Vehicle Velocity Change)(msec)" and the translated value there of "104 (ms)." What this says is that the airbag control module (ACM) captured acceleration and then calculated the delta-V out beyond 104 ms, however, at 104 ms, the ACM found the Maximum delta-V which was recorded and rounded to the nearest 1 km/h for the resolution called out in 49CFR563, -77 km/h (-48 mph).6 Given that we will use the average the weights of the two involved vehicles as provided and the reported delta-V here of the Tahoe – that reported as the "Maximum DeltaV, Longitudinal" – and then apply that to Equation 2, we would find that the delta-V for the LaCrosse should be about 72-73 mph (116-117 km/h).

Equation 2 The answers then to questions 1 and 2 should be the answer to the question: "Did you find any errors in the data as you read through the data?" Yes, apparently there is "something unusual going on."

But the answer might just as well be "well, it might seem so, but..." There is obviously a discrepancy between the recorded delta-V for the vehicles when they are compared to one another but is one or the other (or both) in "error?" If an "error" is "something incorrectly done through ignorance or carelessness; a mistake"7 does that definition fit what's going on here? Does a computer do something as a function of ignorance or carelessness? Does a computer actually make "a mistake" in that context? Not to lose sight of the fact that the ACM is a computer, we need to take a closer look at what it's doing here and ask: if the ACM is measuring acceleration and calculating delta-V from that captured acceleration, is there a way to detect whether or not there's been a "mistake" in the calculation?

One way of addressing that here is to go back to Table 1 and look at the result of separately integrating the acceleration over time found in the crash pulse data table provided as part of the LaCrosse data translation report. The calculated values, while not precisely the same, are close enough to suggest that the calculation for delta-V is being done correctly from the available data captured by the ACM. So, there's no real suggestion that the apparent "error" is in the calculation being done by the ACM and given the history of this technology, we really shouldn't expect there to be.

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The same thought process might be applied to the Tahoe data: could it be "in error?" Using the same type of analysis, we can take the acceleration over time available in the Tahoe data translation report and we see the same minor difference between the ACM calculated 10 ms period delta-V values and that which we might find using the acceleration provided separately at 2 ms intervals with the same, small 2-3 km/h variance. Again, nothing there to suggest an "error" in the calculations.

Still in all, one of the delta-Vs has to be somehow incorrect. The follow up question then might be: if not an "error," not a "mistake," why not? What is going on? While addressing the semantics of the question, another definition for error is: "the difference between a computed or estimated result and the actual value, as in mathematics..."8 In an effort to uncover whether it's the Tahoe delta-V or the Buick delta-V we need to examine more closely, we might next approach this from the perspective of: which delta-V makes sense in the context of the rest of the information, the rest of the data available?

3. If we adopt the facts as laid out in this problem statement including, for the case problem, adopting the average weights of the involved vehicles and the delta-V for the LaCrosse as found in the EDR data translation report, what would the closing speed/velocity for the LaCrosse approaching the Tahoe be at impact?

There are a number of ways to describe "Closing Speed" or "Closing Velocity" where the terms might really be used interchangeably but here, we'll adopt about as simple a definition as there would seem to be: "Closing speed is used to indicate the speed of approach of two colliding bodies immediately before contact."9 Closing speed is a system based quantity and is further addressed in Schmidt, et al 10 in a context which might be applied directly to this Case Problem.

Where we know the weights of the involved vehicles approaching the collision on a single axis, the delta-V of one vehicle or the other, and make an estimate for the potential for restitution in the collision, we find where Schmidt, et al, proposed then tested the model found as equation 3 where the subscript 1 and 2 might be used to identify either vehicle in this analysis to find the closing speed in a single axis, in-line collision.11

Equation 3

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Here, we know – based on the video from the gas station security camera and the EDR data from the Tahoe – that the closing speed found using either vehicle's delta-V will be the impact speed of the LaCrosse because the Tahoe was stopped before the crash.12 Using, as question number 3 proposes, the reported deltaV for LaCrosse (48 mph (77 km/h)), the average weights of the involved vehicles as previously discussed, and an estimate for restitution of 0.1 we find that the closing speed of the LaCrosse "should be" about 72-73 mph (116-117 km/h) as seen in Equation 4. As an interesting aside, if one were to simply discount restitution – "it doesn't really make a difference in 'big delta-V' crashes" – the closing speed, given the information at hand, would be calculated to be 79-80 mph (128-129 km/h).

Equation 4 4. Is the closing speed/velocity for the LaCrosse approaching the Tahoe calculated using the LaCrosse delta-V consistent with the LaCrosse's pre-crash data as it relates to "Speed, Vehicle Indicated" at the last sample before algorithm enable or "time zero?"

In the Case Problem statement, the reader was told that the question, of course, calls for a yes or no answer and, to move the Case Problem statement along, that answer was given in the statement as: no, it is not. To further explain

the answer, we might return to the LaCrosse pre-crash data.

As seen in Figure 1, the LaCrosse was approaching the Tahoe, still accelerating at about 109 mph (176 km/h)13 in the last sample of Pre-Crash data (labeled as time -0.5 sec) captured by the ACM before "time zero," here, algorithm enable or the start of the airbag control's module's deployment decision making algorithm. Using the reported delta-V for the LaCrosse to calculate the closing speed/impact speed of the LaCrosse doesn't work back to the reported pre-crash speed nearest impact. In short, we would all agree that 72 mph is not the same as 109 mph…

So far, we've seen that there's a discrepancy between the LaCrosse reported delta-V and the Tahoe reported delta-V when the two are considered one-to-the-other in the context of force balance and where we would expect the deltaVs to be inversely proportional to their weights. Using the reported LaCrosse delta-V to solve for the LaCrosse closing speed/impact speed comes up significantly low of what is reported separately in the LaCrosse Pre-Crash data. The next logical step would be to calculate closing speed again, but this time use the Tahoe reported/recorded delta-V. 5. If we adopt the facts as laid out in this problem statement including, for the case problem, adopting the average weights of the involved vehicles and the delta-V for the Tahoe as found in the EDR data translation report, what would the closing speed/velocity for the LaCrosse approaching the Tahoe be at impact?

Using, as question number 5 proposes, the reported deltaV for Tahoe (48 mph (77 km/h)), the average weights of

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the involved vehicles as previously discussed, and an estimate for restitution of 0.1 we find that the closing speed of the LaCrosse "should be" about 109-110 mph (175177 km/h) as seen in Equation 5. Again, as an interesting aside, if one were to discount restitution – because "it doesn't really make a difference in 'big delta-V' crashes" – the LaCrosse closing speed, given the information at hand, would be calculated to be 120-121 mph (193-195 km/h).

Equation 5 6. Is the closing speed/velocity for the LaCrosse approaching the Tahoe calculated using the Tahoe delta-V consistent with the LaCrosse's pre-crash data as it relates to "Speed, Vehicle Indicated" at the last sample before algorithm enable or "time zero?"

Referring back to Figure 1 – the LaCrosse's Pre-Crash data, it should become clear that the calculated 109-110 mph (175-177 km/h) is a much better fit. This suggests that the Tahoe delta-V is actually "right on" insofar as where it is used in the closing velocity calculation, we find the speed reported in the LaCrosse data translation report.

In another way of thinking about this, we might use the delta-V calculated looking at force balance based on the Tahoe delta-V to calculate closing speed for the LaCrosse.

That done as seen in Equation 6, we find that the closing speed of the LaCrosse "should be" about 110-111 mph (177-179 km/h).

the LaCrosse should really be about 72-73 mph (116-117 km/h).

Another way to approach this problem, in what's described in Schmidt, et al as a gedanken or thought experiment, we might assume the Tahoe to be stopped and the LaCrosse is approaching at some speed striking the Tahoe aligned as described. If we adopt their delta-V values and LaCrosse Pre-Crash data as reported, then the LaCrosse (at about 109 mph (175 km/h)) struck the Tahoe and was slowed by -48 mph (-77 km/h) meaning the LaCrosse was still going some 61 mph (98 km/h) after impact. Adopting then the Tahoe delta-V where the Tahoe was stopped at impact and experienced a -48 mph (-77 mph) delta-V of its own, it would be leaving the area of impact, going backwards, at about 48 mph (77 km/h).

Looking at the overhead scene photo, those numbers beg the question: in what way would it be possible for the Tahoe, going backwards, essentially, at least briefly a "hood ornament" on the LaCrosse, to be traveling at 48 mph (77 km/h) when the LaCrosse was traveling at 61 mph (98 km/h) post impact? Put another way: "how does the hood ornament get to be going slower than the hood?" clearly, that doesn't work out.

Trying the same thought experiment again but this time using the LaCrosse delta-V as calculated at about 72-73 mph (116-117 km/h), that puts the LaCrosse out of the collision at 36-37 mph (42-44 km/h) where the Tahoe was traveling at more like 48 mph (77 km/h). Although that seems like a pretty significant separation speed that's still in keeping with an estimate of restitution at about 0.1 where we take the absolute value of the separation velocity over the closing velocity.

8. Given the information available in the Case Problem statement, what should the delta-V of the LaCrosse be?

Equation 6 7. The delta-V for the LaCrosse is apparently [under-reported] [over reported] (select one, what did you find?).

In the Case Problem statement, the next question asks the reader to choose a response to the question: is the LaCrosse delta-V under-reported or over-reported? At this point, it should be fairly clear to all that the LaCrosse delta-V data is under-reported. In the data translation report, the LaCrosse maximum delta-V is reported as 48 mph (77 km/h) but, looking at this problem from the perspective of force balance using the delta-V of the Tahoe, the delta-V for 98 Collision Magazine - Volume 15 Issue 2

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This seems like it might have already been answered, but to be clear at this point in this Case Problem solution, given the data available, the delta-V for the LaCrosse should be about 72-73 mph (116-117 km/h).

9. One should be prepared to explain the rationale behind the answer to question #8. In other words: how do you get to your answer for #8? (the wrong answer is: "just because that's what it says so in the Pre-Crash data…")

To really address question #8, we should turn back to the analysis of the delta-V for each vehicle when comparing them one-to-the-other and then the reasoning behind why the LaCrosse couldn't have had "only" a -48 mph (77

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km/h) delta-V. There might be a school of thought that might argue, being perhaps as morally flexible as possible, that all the data – the delta-V, the Pre-Crash, the deployment timing, the belt use, everything – as retrieved and translated from both vehicles are all wrong: "in error. " To adopt that all the various vehicle systems – independent and interrelated – in two separate and independent cars all failed at just the same time and in the same way is beyond unreasonable. Yes, there's "something" wrong with the delta-V as reported by the LaCrosse, but that doesn't mean, for example, the Pre-Crash speed – which originates from a different system in the vehicle – would be affected by some sort of failure or malfunction. The oft heard complaint "well, if one's wrong they must all be wrong" is without any reasonable support and flies in the face of logic.

We can't escape that there was an underreporting of deltaV in the LaCrosse and that needs to be explained but, at the same time, we might look at the data and point out that none of the delta-V for either vehicle is OVER-reported. Moreover, we have other options for working to corroborate the actual speed of the LaCrosse at impact.

For example, we might consider a one axis, one vehicle moving in-line momentum solution. Where we again use the average weights of the involved vehicles and adopt that the Tahoe moved about 172 feet (54 m) backwards – predominantly on the paved roadway – and the LaCrosse moved about 228 ft (69 m) from impact where the last 30 ft (9 m) of that was at least partially off the road and onto loose gravel before coming to rest. We might run some tests to find the available effective friction for these vehicles coming to rest or, for the moment, we might adopt that the Tahoe and LaCrosse left impact at about the speeds previously offered such that that gives us a "starting place."

Then with the LaCrosse post impact speed at about 36 mph (63 km/h) and the Tahoe at 48 mph (77 km/h), we might calculate that the impact speed of the LaCrosse would be about 109 mph (175 km/h) as seen in Equation 7.

soned that the LaCrosse delta-V is under-reported, the remainder of the data – across both vehicles - "makes sense" together.

10. What might the reason be for the discrepancy with the LaCrosse delta-V data?

This is the topic where the solution discussion might go in a number of different directions. One school of thought might be to say that there was a vertical component to the delta-V of the LaCrosse as a function of the car's pitch: the rotation of the LaCrosse around its lateral axis where we see, in the video stills, the rear of the car coming up off the ground. The underlying notion would be that, as a function of that rotation, the vertical component isn't (can't) be measured and that's why the longitudinal deltaV is under-reported (keep in mind the lateral delta-V is so small as to be insignificant to this analysis therefore it's left out of the discussion here).

There's no doubt when one compares the alignment of the LaCrosse on the road just prior to impact in a still frame from the gas station security camera to a frame captured during the crash pulse (Figures 2s and 2b) it is clear that there was significant rotation of the LaCrosse around its lateral axis during the crash: undeniably, there was a lot of negative pitch.14 In terms of the angle of the floor pan of the car to the roadway, there's no doubt that the rear of the car came off the ground a significant distance but, where the delta-V on any axis might be measured, is that pitch re-

Equation 7 The post impact distances of 172 ft (54 m) and 228 ft (69 m), for the Tahoe and LaCrosse respectively, are generally consistent – on the roadway seen in the photos – with the post impact speeds of 39 mph (63 km/h) for the LaCrosse and the Tahoe at 48 mph (77 km/h).

In then another approach to addressing whether or not the pre-Crash data for the LaCrosse might be right, we see where if we adopt the information at hand and have reawww.collisionmagazine.com

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Figure 3 ally going to "create" some sort of "vertical delta-V" which can or will be measured by the ACM? As seen on the approach in the video frames, there may be some "lift" in the body of the LaCrosse or perhaps some body roll as a function of the lane change into the Tahoe's lane. The LaCrosse's alignment in a planar sense, relative to the road (as seen in Figure 2a) – including the slight downhill grade in the road as seen in the video still frames – makes it fairly easy to compare that to the rotation around the lateral axis (pitch) as seen in Figure 2b.

Figure 2b was selected as being the last frame in the gas station video which could still have been captured at least partially during the crash pulse where that would be, given the video running at 30 frames per second, about 130-150 ms after impact and slightly less than that from "algorithm enable" or "time zero."

But if we start talking about acceleration or, in some way a "delta-V" on the vertical (normal) axis, how does that square with the portion of the LaCrosse report which reads as seen in Figure 3? Doesn't it say, "Vertical Crash Pulse" – "Contains No Recorded Data?"

OK, most readers probably caught it right away, but Figure 3 is a "red herring," yes, it is a "deliberately misleading clue that diverts attention from the real explanation." Figure 3 is one of the data elements this ACM might record in a rollover event. This crash isn't identified by the ACM as a rollover event and therefore, there is no and there would be no vertical data recorded for the subject FSR event. So, that is not the answer…but the figure was thrown in here to see if the reader is closely checking what the data translation report really says.

in a crash – again setting aside a rollover event – on the longitudinal and lateral axes. These axes are, since the module is, "vehicle fixed."

Vehicle fixed here means that no matter what the orientation of the vehicle to the roadway plane may be – and, for the most part by extension the ACM – that relationship is fixed ACM-to-vehicle. So that means that if the car is siting on a flat surface ("flat" meaning a single plane here, there can be an "up hill" or "down hill" grade) the longitudinal axis is approximately parallel to the roadway plane as seen in Figure 4. In that otherwise neutral position, in that orientation to the roadway, the accelerometer measuring longitudinal acceleration is "looking" fore and aft along the longitudinal axis. In that position, the accelerometer measuring longitudinal acceleration for, at some point, longitudinal delta-V would never see anything acting vertically relative that car. That would be at 90 degrees to the longitudinal axis and out of the "window" for the longitudinal accelerometer.

If the car pitches "a little," for example, during heavy braking, the effect on where the ACM's accelerometer is "looking" is negligible. Let's assume there's negative pitch where the accelerometer is now "looking" 5 degrees below its original alignment (such as might be seen in Figure 5), that means it's still going to "see" more than 99% of what it would have seen along the original longitudinal axis during the crash pulse (given that the cosine of 5 degrees is 0.996).

In other words, it's going to take "a lot" of pitch to realign the longitudinal accelerometer to a position such that it only "sees" a delta-V which is going to be reported as 48 mph (77 km/h) but should really be "seeing" about 72 But the actual answer is in the form of another question: mph (116 km/h). In fact, the pitch at the ACM would where does the data we are looking at coming from? That have to be such that the car would have had to have romeans, consider where the LaCrosse longitudinal delta-V tated to something on the order of 48-49 degrees from the we all should reasonably now agree is under-reported is original neutral zero to report what should be a 72 mph coming from. The answer to "where" is the accelerometer (116 km/h) delta-V as the under-reported 48 mph (77 in the airbag control module which captures acceleration km/h) delta-V. 100 Collision Magazine - Volume 15 Issue 2

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Another way to think about this is: as that car pitches that 5 deg, does it see some sort of acceleration vertically? Perhaps it might capture some where, as the car is rotating with the rear "coming up," the angular acceleration – as potentially measured by the longitudinal accelerometer looking at the linear component – might appear to be acting slightly in the negative direction on the ACM's longitudinal axis but it won't be much. In other words, that rotation and angular acceleration are not going to be the source of under-reported acceleration in the negative direction on the longitudinal axis.

On the contrary, since the rotation is acting to create effectively what might be measured as front-to-rear (negative) acceleration on the longitudinal axis it might increase the apparent negative acceleration the accelerometer captures. For the longitudinal accelerometer, the rotation/pitch is probably "adding" an amount – a small amount – to what's being captured as negative (longitudinal) linear acceleration rather than taking away from it.

Figure 4

Figure 5

Two ways to track and quantify the pitch of the LaCrosse (and the attached ACM) were made using the publicly available Tracker Software.15 Tracker software has been used successfully to identify and track positions in crash videos in other similar analyses including that seen in Haight, S., et al16 previously in Collision. In this instance, the two tracks were made such that one followed the center of the LaCrosse's trunk as the car pitched to estimate the overall angle of the car and the other tracked the approximate location of the ACM to approximate the orientation of that module to the ground and relative to where it might be "looking." In Figure 6, The approximate position of the ACM, considering the angle of the car in the frame from the gas station video and aspect ratio to the car, is identified in the crosshairs. The crosshairs are not aligned with the plane of the roadway, they're present in that frame in the Tracker software to mark a position.

Figure 6

In Figure 7, starting where the ACM was located in Figure 6, the small dots indicating positions 1, 2, 3 4 and 5 are approximations of the position of the ACM across the next 5 gas station video frames representing a total

Figure 7

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mean the reported delta-V should still have been about 64 mph (103 km/h). Of course, that's not the case; the deltaV is reported as 48 mph (77 km/h).

Which brings us back to the notion that there might be a vertical component which might account for the "missing" portion of the delta-V. That notion fails when we look at the track of the ACM in Figure 7. The car, on the whole, rotates as seen in the still frames which are part of the Case Problem statement and as a function of that seen in Figures 7 and 8 in this solution, but that rotation doesn't actually "lift" the ACM on the vertical axis in the sense that there would be vertical acceleration at the ACM and such that it would account for much if any portion of the underreported vehicle fixed longitudinal delta-V.

Figure 8 of about 0.15s – slightly longer than the crash pulse at 104 -138 ms for the two cars but keeping the series of individual video frames in order and intact.

For Figure 7, a protractor has been superimposed over the frame which corresponds to the angle of the car and the direction the longitudinal ACM would be "looking" in various positions. The protractor "zero" is aligned with the original neutral position of the LaCrosse relative to the road as a baseline plane. Notably, the position of the ACM relative to the ground doesn’t really change much, the pitch of the car body overall, however, does. The pitch of the car body generally corresponds to the angle the ACM is "aimed" or "looking at" if all we're considering is rotation of the car as a mass.

In Figure 7, the approximate angle of the ACM's longitudinal axis is rotated "downward" to about 22 to a maximum of 30 degrees below its original planar alignment with the road pre-impact.

In Figure 8, the last two positions of the LaCrosse's trunk (points 4 and 5) are seen in the upper portion of the frame capture at left and the protractor is aligned with the floor of the car. This creates an angle of about 27 degrees "downward" from the original planar alignment with the road pre-impact.

Using the Tracker software, it appears that the car pitched up to about 27-30 degrees during the crash pulse – during the period where pitch might have an impact on the captured acceleration. Assuming roughly the middle of that range – at 28 deg – the ACM's longitudinal accelerometer was still "seeing" about 88% of what would be going on relative to the original longitudinal axis. If we apply that to the 72 mph (116 km/h) longitudinal delta-V, that would

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During the crash pulse, the ACM is being rotated away from "looking" along the original longitudinal plane; away from its original orientation/position relative to the roadway. By way of analogy, think of looking at an object through a tube. In the initial viewing position with the tube parallel to the ground, one can see a whole object, top-to-bottom through the tube. Now, while still looking through the tube, start to lower the end of the tube away from your eye looking increasingly "downward" relative to the tube's original position and see how much of the original object you can still see as tube moves further downward from the original position. That portion one can see through the tube as it starts to look "downward" is all that can be captured and reported as a view through the tube. The same applies to the ACM's accelerometer. It's looking down the car's longitudinal axis and as that "look" moves off the original plane, what the accelerometer "sees" becomes a portion of what was originally available ("visible").

As we'll see, there is another component to this which should be addressed but the answer is not that there was a vertical component because of the rotation which is being "missed" by the ACM. Rotation doesn’t necessarily equate to significant vertical acceleration, a portion of which might be measured at the ACM and, if it is, it might actually add to the delta-V as previously noted.

Another school of thought would suggest that part of the reason for the delta-V to be under-reported was the pitch of the LaCrosse, however, an additional component of that under-reporting was a result of the deformation of the car's floor in and around where the ACM was mounted.

The idea is that the deformation was such that, in addition to pitch changing the orientation of the car and attached ACM during the crash pulse such that it was "looking" about 27-30 deg off the original plane, there was also a point where the engine being pushed rearward and then,

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by design, largely under the floor of the car, under the "tunnel," it would distort the area where the ACM was mounted causing additional off-axis ACM orientation and, as a result, additional underreporting of the acceleration and delta-V. In Figure 9, we can see across the front and a portion of the right side of the LaCrosse. The first impression might be: "where did the engine go?" In Figure 10, we can also see the full front of the LaCrosse and, while we see what are clearly parts of the engine, one has to wonder: "where did the engine go? " Of course, the engine is compressed but, by design, it collapses rearward under the occupant compartment of the car which necessarily means it will be displacing at least some portion of the interior normally starting with the tunnel. In such an extreme case, the engine also distorts the floorboard in front of the front occupant positions as can be seen in Figures 11, 12 and 13. In Figures 11 and 12 one may see the floor around the area of the driver footwell and, in particular note where the pedals are visible, the rearward distortion of the floor (toward the driver). Considering Figure 11, imagine the area around the accelerator pedal and that pedal's current alignment compared to where one would expect to find it in the car. As seen in Figure 11, the apparent shortened distance between the floor which would normally be behind the pedals and the driver seat might be compared with an undamaged similar view in composite Figure 14 for comparison.

In Figure 13, we see the distortion of the center console and tunnel from the other side of the car which might be compared to that seen in Figure 12.

Figure 9

Figure 10

In both Figures 11 and 12, one may also notice the distortion of the center console which would have been over the tunnel and the rearward displacement toward or to, at least, the leading edge of the driver's seat.

The ACM is mounted in a position on the center tunnel as approximated in

Figure 11

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Figure 12

Figure 13

Figure 15 first in the sketch according to the GM service manual for the airbag system (#8 – ACM) and then in the two photos of the undamaged exemplar and the damaged center console for the subject LaCrosse, lower right and then left, respectively. The position of the ACM is approximated in the photos but is generally representative of the area, position and orientation of the ACM in the LaCrosse.

ponents which otherwise amplified the under-reported longitudinal delta-V? Yes, there is.

It becomes clear that the LaCrosse being distorted rearward also changed the position of the ACM relative to the remainder of the car and, given the rearward then upward thrust of the components mid-tunnel as visible in Figures 11-15, that would easily have "re-aimed" the ACM's longitudinal axis further "down" relative to the original longitudinal plane for the ACM. If the pitch accounted for roughly half of the necessary off axis reorientation of the ACM, the distortion seen in Figures 11-15 could surely have made up the other half.

But is there any other information which might support the idea that there was a displacement of the vehicle com104 Collision Magazine - Volume 15 Issue 2

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Consider the acceleration data found in the Crash Pulse data table in the LaCrosse EDR data translation report and that for the Tahoe data translation report. Of course, the delta-V is calculated from the captured acceleration and there's already been the discussion of the lower resolution display for the acceleration data in the data translation report, but there's something "unusual" about the appearance of the LaCrosse acceleration data which is noticeable particularly when compared to that for the Tahoe.

In a review of the gas station video using the Tracker software, the rotation of the car to its apogee was remarkably smooth, almost uniform, during the crash pulse. There wasn't a point where the pitch of the LaCrosse, as a larger unit increased "more rapidly" than another during the crash pulse as seen in the gas station video. There wasn't a point where, in the gas station video, the rear of the car rose or rotated "up" more rapidly than any other point such that there might be a sudden "spike" in the data from

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Figure 14

Figure 15 more rapid rotation/pitch. However, in the LaCrosse's ACM longitudinal acceleration data there is a point where the acceleration appears to "spike upward" from what would be a "normal" appearing wave shape.17 Figure 16 is the Crash Pulse data table acceleration data from the Tahoe EDR data translation report where the raw

data has been exported to Excel and then graphed for this and the following figures truncating the data at 180ms – outside the crash pulse – to allow for larger graphs.18 With that, the Tahoe's delta-V as found in the Crash Pulse data table for the Tahoe's data translation report has been superimposed over the acceleration where they are two separate tables in the data translation report. The wave shape seen

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That does, however raise another issue. Where the Tahoe acceleration is clipped, necessarily the acceleration and, therefore, the delta-V is going to be under-reported. There being three identified points in the data where the accelerometer reaches -51g, the amount of clipping is likely relatively small, however, it can't be denied that, as a limitation of the physical range of the sensor (here that sensor being the accelerometer), the delta-V for the Tahoe is going to be under-reported.

Figure 17 is the Crash Pulse data table acceleration data from the LaCrosse EDR data translation report where the raw data has been exported to Excel and then plotted in Dplot 20 (as is the case for Figure 16 and the following figures 21). With that, the LaCrosse delta-V as found in the Crash Pulse data table for the LaCrosse's data translation report has been superimposed over the acceleration where they are two separate tables in the data translation report. The wave shape seen in Figure 17 does not look like one would expect in a crash and does not look at all similar to that seen in Figure 16.

Figure 16

Figure 17 in Figure 16 looks much like one would expect in a crash, particularly one of this severity. There is nothing really unusual about it other than the observation that, more than once, the accelerometer was saturated and the acceleration data is clipped at about -51g 19 - again, not unexpected in a crash of this severity.

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There is an otherwise unusual "spike" in the data at about 64 ms into the crash pulse such that the captured acceleration "drops" from about -42g to -17 g then -0.2g. By comparison, the lighter LaCrosse never reports acceleration at the same level as seen in the Tahoe, however, that doesn't mean that the LaCrosse acceleration wasn't clipped although the reported range for the LaCrosse accelerometers is given as "approximately 50g's 22."

With the magnitude of this head-on crash, it is not surprising that both ACM's accelerometers would be saturated so

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Figure 18 that their acceleration data was clipped – it exceeded the range of the physical sensor.

Given that Newton's Third Law requires equal but oppositely directed forces to be applied to each vehicle during their contact, perhaps it is not unexpected that the timing of high collision forces producing high acceleration levels would roughly correspond and be evident in both vehicles' accelerometer data. As such, it appears that AE for the Tahoe occurred approximately 7 ms after AE for the LaCrosse. Then aligning the three regions where the ACM accelerometers bottomed out or saturated results in an apparent 7 ms offset in the data sets.

Reviewing Figure 17 (and the underlying acceleration data found in the data translation reports) closely clearly shows three distinct "flat spots" in the LaCrosse crash pulse acceleration data at 42.2 g's each. It is entirely possible that these represent a point were the accelerometer has reached the actual limits of its range ("approximately" 50g's) not to mention the potential for over-ranging and the possibility of physical damage to the accelerometer in the crash.

also compare the condition of the interior of the involved vehicles and consider the discussion regarding the position of the ACM in the LaCrosse and comparable position of the ACM in the Tahoe as seen in composite Figure 18 and leave that question for "cross examination" later…

Returning to Figure 17, where one would expect to see the LaCrosse crash pulse more "smoothly" return toward "0 g's" like that seen in Figure 16 we might ask, what's going on dynamically with the LaCrosse about 60-70 ms into the crash pulse? As close as we can see in the gas station video, the LaCrosse is, at that point between frames 0+1 and 0+3 (frame counts) and the rotation, the pitch, where the rear of the LaCrosse is starting to lift is beginning in earnest (Figure 19). The time top to bottom views between the frames in Figure 19 is about 60 ms where the frame on the

This all begs the question: so, could it be that the crash really was more severe for the heavier, stiffer Tahoe and the delta-V – the 48 mph (77 km/h) delta-V for the LaCrosse – was "right" and the Tahoe delta-V is over-reported (rather than the delta-V for the lighter LaCrosse being underreported)?

After taking a moment to re-read that to be sure the reader didn't misunderstand the "question," while we've seen the exterior of the involved vehicles in previous photos and we've seen still frames from the gas station video, we might www.collisionmagazine.com

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top, nearer the onset of the crash, is about 30 ms into the crash pulse making the one on the bottom closer to 90 ms after the onset of the event.

As the pitch over increases, it would follow that the LaCrosse has "bottomed out" on the Tahoe and, at that point, the ACM is "looking" less and less along the original planar longitudinal axis not only because of the pitch – which only accounts for about half the underreported delta-V – but also the displacement of the center tunnel further distorting the "ACM's accelerometer view" along the original plane. Under-reporting of the LaCrosse deltaV in the EDR data translation report can occur as a function of a number of separate and potentially overlapping mechanisms including, a large amount of vehicle pitch, floor deformation causing additional rotation of the ACM relative to the vehicle's longitudinal axis, and accelerometer saturation producing clipped acceleration data. Overranging an accelerometer thereby altering its sensitivity, internal ACM damage, or intrusion that detaches the ACM from its vehicle mounting (decoupling it from the vehicle) are additional mechanisms by which the ACM reported delta-V could understate the actual delta-V.

to the "Event Data (Event Record 1)" for each, we see that the seat belt pretensioner was deployed in the LaCrosse for the unbelted driver at 2 ms after "time zero" and the driver first stage airbag was deployed at 3 ms after "time zero."

That would then be compared to the data from the data table of the same name from the Tahoe where we would see that the belt pretensioner for the belted Tahoe driver was commanded for deployment at 13 ms and the first stage airbag was commanded for deployment at 13 ms as well.

That otherwise small time difference – 9-10 ms – reflects the different characteristics of these vehicles, different conditions (belted versus unbelted) the ACM was "working with" and how much earlier the smaller, lighter LaCrosse ACM might have begun "working the crash problem" compared to that of the heavier, stiffer Tahoe with the belted driver. The "time zero" for each, while undeniably close in terms of "global time," would have been ever so slightly later in the Tahoe than in the LaCrosse but, in this crash, given the severity, the difference seems to be very close to 7ms as seen in the adjusted Figure 21.

We can see more clearly the difference in the overall shape of the acceleration trace using the accelerometer data for As a further comparison between the acceleration captured the LaCrosse compared to that of the Tahoe in Figure 20 by the two vehicles, in Figure 20 we might compare the and, given the dynamics observed in the gas station video crash pulse for the Tahoe with that for the LaCrosse. The as well as the condition of the interior of the LaCrosse, we initial difficulty might be finding a common "time zero" can safely conclude that the LaCrosse delta-V was underbetween the two ACMs to align the acceleration data. Giv- reported consistent with the previous estimates made usen the separate stiffness, mass, and other characteristics of ing force balance and calculating the closing speed/impact these two vehicles, we can't expect that the ACMs would speed of the LaCrosse using the Tahoe's delta-V. have, all things considered, the same "wake up" or "algorithm enable" point, however, in a crash of this severity the Which bring the Case Problem back to a previously expoint in "global time" can't be too dissimilar. In Figure 20, plored point: is the data "in error?" Does it make the data the acceleration data from the two individual ACMs in the "unreliable?" No doubt, the LaCrosse delta-V is inaccurate as reported. The LaCrosse delta-V is undeniably undertwo vehicles is represented with a common "time zero." reported but can that be called an irredeemable "error"? A But what if we are to propose that the largest spikes in the "mistake" casting doubt on all the remaining data? Or is two data sets – where we see the Tahoe data reaching the this an example of an "error" being a function of the expli51g point and the LaCrosse data reaching the 42g point – cable limitations of a machine and the similarly explicable were common to both vehicles with respect to time? What resultant "difference between a computed or estimated reif there was something going on in both which would pro- sult and (an) actual value...23"? duce not one but three acceleration spikes at about the same frequency and we align the data to those points? The result Uncovering the reason be for the discrepancy with the would be the adjusted Figure 20 now seen as Figure 21 LaCrosse delta-V data also reinforces the reliability of the which requires an offset for "time zero" across these sys- Tahoe data and leads to the explanations here and reinforces the independent reliability of the remainder of the transtems of only 7 ms. lated data for both vehicles. There was nothing wrong with Another way of evaluating the difference in the behav- the calculations done by the LaCrosse ACM in terms of ior of these two systems might be to consider when belt calculating and recording the delta-V of the LaCrosse even pretensioners and airbags were deployed for each vehicle though it is demonstrably under-reported. The LaCrosse individually (keeping in mind also driver belt switch sta- ACM was working with the information it had available tus). Returning to the data translation reports for the two constrained as a function of the limitations of the device vehicles found in the Case Problem statement and looking under the conditions and given the dynamics of the instant 108 Collision Magazine - Volume 15 Issue 2

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crash. The Tahoe ACM didn't suffer as much from similar limitations (save for the clipping noted) and was able to record sufficient information to allow for a more complete analysis and way to satisfactorily explain the LaCrosse data anomaly.

11. Going back to the suggested line of cross-examination questions, where we find "Algorithm Active: Rear" and then, to the right it says "Yes" in the System Status at Deployment data table, for this full overlap frontal crash, why would the rear algorithm be active in this case?

Figure 20

The reader may have thought this last point forgotten in the detailed discussion of the LaCrosse delta-V data but, no, here it is….why did the LaCrosse ACM report that the rear-end crash algorithm was active during the undeniably very frontal event?

For reference, from the LaCrosse data translation report as found in the "System Status at Event (Event Record 1)" data table, we find the entries seen again here in Figure 22.

There are 4 data elements in Figure 22 indicating which of the ACM's "Algorithm(s are) Active" during a recorded event. From the LaCrosse in this case, we see that the "Frontal," "Side," and "Rear" (FSR…) algorithms were, at some point during the event, active. We're not told how long, in terms of time, the algorithms might have run, or when, in the larger timeline they ran but we can conclude that, at some point, these three "branches" of the ACM's protective algorithm(s) ran. The question is, of course, in this frontal crash, why would a "Rear(ender)" algorithm run? For that matter, given the clearly insignificant lateral delta-V (-2 mph (-3 km/h)) recorded in this event, why would the "Side" algorithm run?

Figure 21

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The shorter answer is: for the data table, for these elements, the ACM is simply reporting which decision-making processes ran, for whatever reason, during whatever portion of the event but the important element(s) are what the "Event Severity Status" is (was) in the event. That tells us what the ACM ultimately acted on.

Dynamically, as the LaCrosse engaged the Tahoe, there was (as evidenced by the small lateral delta-V) some lateral effect on the car and, for that reason, the ACM considered the question which might essentially be framed as: "is there anything the ACM need to do here for the occupants related to a side impact?"

The same could be said as it relates to the "Rear(-ender)" algorithm. At some point there was something sensed in a positive longitudinal direction which prompted the ACM to essentially address a question: which might be framed as: "is there anything the ACM need to do to protect the occupants as a function of something it is sensing acting on the car rear-to-front?"

Some readers may have sensed where this is going… What part of this crash seems to have acted rear-to-front on the LaCrosse?

There's a good chance that the pitch, particularly once the car reaches its apogee resulted in some then positive acceleration on the longitudinal axis which, if it's happening during the crash pulse, would have further acted to under-report the LaCrosse's longitudinal delta-V. This, of course, might offset any small potential angular rotational negative longitudinal acceleration picked up by the ACM during the pitch over and really reduce the majority of the reason for the under-reported longitudinal delta-V to (1) the redirected "focus" of the ACM's longitudinal accelerometer and (2) the distortion of the portion of the ACM as mounted in the car.

But, as noted, the significant element(s) which are a product of which algorithms may have run is the indication of which "Event Severity Status" elements indicate "yes."

Here, for the LaCrosse, we see that "Frontal Pretensioner," "Frontal Stage 1," and "Frontal Stage 2" are all marked "yes." This is, together with the "Algorithm Active" elements, an indication of (1) what the ACM "sensed" and was "working on" and then (2) what "it did" as a function of that analysis.

Summary For each individual reader there will likely be different "takeaways" from this Case Problem and the analysis of the EDR data. If there are a couple major points that might stand out, they should probably be: 110 Collision Magazine - Volume 15 Issue 2

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1. Just because there's one element or type of element that seems to be otherwise anomalous, that doesn't mean the rest of the data is unreliable One should never fall into the trap of worrying about the cross-examination question that sounds something like "well, if THAT element's wrong, then who is to say that ALL the elements aren't wrong? " Data elements in this technology are individual, stand-alone elements often coming from completely independent computers or sensors in a car. An anomaly at one device showing up in one element might be related to an anomaly, physical or mechanical limitation or dynamically influenced condition effecting another element but the idea that one is "unreliable therefore all the others are unreliable" is based on nothing less than ignorance. If this Case Problem did nothing else, it should have reinforced the idea that looking deeper into the data – all of the data – often results in an explanation of what would otherwise seem to be anomalous data and may, in some cases, strengthen the understanding and reliability of other, remaining elements.

2. Look at the entire CDR Tool data translation report Human nature being what it is, we can't let ourselves fall into the trap of focusing on "what's most important to me" while disregarding the rest because it's for "the 'civil case'," or "the 'criminal case'," or "I don't care about (for example) the delta-V" or "DTCs are just for civil cases…"

Every part of a CDR Tool data translation report is open to the question: "Did you find any errors in the data as you read through the data?" When we ignore one part or another or just don't look for the seemingly "strange data," the data that "doesn't appear to make sense" – whether or not it's part of the focus of our immediate analysis – we're going to miss something and we should assume that "the other side" will have seen it and is working on ways to exploit it no matter how ethically liberal the argument or opinion would have to be.

Footnotes 1. FSR – "Front, Side and (or) Rear" events as defined in the La Crosse and the Tahoe Data Limitations.

2. From the Data Limitations test: "The GM parameter name is displayed in parentheses after the NHTSA Part 563 parameter name." In other words, in this case, 49CFR563 calls for the data element "Maximum Delta-V, Longitudinal" and GM has added, parenthetically, their historical/typical name for this element "Maximum Longitudinal SDM Recorded Vehi-

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cle Velocity Change for FSR Event)." Again, "FSR" is defined, also in the Data Limitations text, as a "Front, Side (or) Rear" event.

3. There is a corresponding "Maximum Lateral" delta-V and time to that Maximum Lateral delta-V, however, for this analysis, the translated value of -2 mph (-3 km/h) isn't terribly significant.

4. See Title 49 of the Code of Federal Regulations, part 563 (49CFR563), "Event Data Recorder," specifically, see 49CFR563.5 Definitions.

5. This point leaves of a further detailed discussion of reporting resolution and rounding found in 49CFR563.

Rounding, up at the 0.5 km/h point and down below that for a speed or delta-V value, the values are then translated to imperial and, for some elements, rounded again. For example, in the LaCrosse data translation report, we see 77 km/h in the crash pulse data table represented as 48.8 mph. At the same time, in the "System Status at Event" data table, 77 km/h is translated to 48 mph. In the end, it is a distinction without a (meaningful) difference and not overly relevant to this Case Problem, but it is worth noting.

6. There is a corresponding "Maximum Lateral" delta-V and time to that Maximum Lateral delta-V, however, for this analysis, the translated value of -1 mph (-2 km/h) isn't significant.

7. Collins Online English Dictionary, 2021 8. ibid 9. McHenry, B., McHenry Forum, posted Fri Jun 22, 2012, https://www.mchenrysoftware.com/board/ viewtopic.php?t=586 10. Schmidt, B, Haight, R., Szabo, T, Welcher, J, "SystemBased Energy and Momentum Analysis of Collisions", paper 980026, Society of Automotive Engineers 11. Where the vehicles approach the collision at something other than a single axis, then the delta-V values have to be resolved for the vectors involved.

12. We also know that the LaCrosse's Pre-Crash speed as recorded is pretty accurate inasmuch as the LaCrosse was being pursued right up to impact.

13. The LaCrosse recommended tire size is given as 235/50R17. The involved LaCrosse tires were winter tires without studs, size 225/50R17. The recommended tire size circumference should be 82.47 inches per rotation but the tires on the car would have been rotating at 84.02 inches per rotation. The difference in the tires on the car versus what was GM recommended would result in an under-reported Pre-Crash speed of -1.84%. That difference, within the normal range of

tread wear, tire inflation, and temperature is of little meaningful consequence for this analysis but, at least bears mentioning. In short, where the LeCrosse PreCrash speed was recorded to the whole kilometer as 176 km/h and converted for the data translation report to 109 mph, the real speed might have been 179180 km/h and converted to 111-112 mph.

14. Adopting polarity as found in SAE J211 and SAE J1733 15. Brown, D, Christian, W, and Hanson, R, "Tracker Video Analysis and Modeling Tool," Open Source Physics, available at https://physlets.org/tracker/ 16. Haight, S., Haight, R, “Analysis of Event Data Recorder delta-V reporting in the IIHS Small Overlap Crash Test,” Collision Magazine, Vol 8, Issue 2, 2013 17. Haight, S., "Basic Integral Calculus for Crash Reconstruction," Collision Magazine, Vol 3, Issue 2, 2008 18. One may compare this and the ensuing Figures to the corresponding graphs found in the Case Problem statement and the original Bosch CDR Tool reports to verify the data hasn't been altered.

19. From the Tahoe Data Limitations text: "The reported range of the longitudinal and lateral acceleration values is approximately ± 50 g." (rev 01050_SDM30delphi_r020) 20. Hyde, David, "Dplot Graphing Software for Scientists and Engineers," Hydesoft Computing, LLC, https:// www.dplot.com/index.htm 21. ibid.

22. From the LaCrosse Data Limitations text: "The reported range of the longitudinal and lateral acceleration values is approximately ± 50 g." (rev 01043_SDM10P-conti_r017) 23. ibid Editor's note: The underlying crash that developed into this Case Problem was investigated by Troop "C" of the New York State Police Collision Reconstruction Unit (CRU). The authors and Collision magazine staff want to thank NYSP Investigator Travis Webster for his work on the investigation and Lt Daniel Bates for his contributions to the reconstruction profession.

As the reader might well imagine, the driver of the LaCrosse didn't survive the crash, however, we are relieved to be able to point out that the deputy who had been driving the Tahoe did survive the crash although he sustained significant and extensive injuries.

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