Volume 13, Issue 2

ollision C

Volume 13 Issue 2

The International Compendium for Crash Research

New biofidelic dummy crash behaviour in a crash comparison

vehicle fire = no electronic data?

tesla edr case studies and reconstruction techniques

infotainment systems use in accident reconstruction

collisionmagazine.com

Crash site evidenCe

A 2010 Chevrolet Equinox SUV attempted to make a left turn in front of a 2003 Dodge Ram pickup. The pickup was in the middle northbound lane when it struck the left-rear side of the SUV, causing both vehicles to rotate CCW to their final rest positions.

The right-rear passenger in the SUV suffered significant facial injury, but there was no physical evidence on the back of the right front seat. How did the facial injury occur?

For more information on the capabilities of HVE please visit edccorp.com

HVE directly answers the following questions using Newton’s laws of motion:

Questions

Answers

Impact Speeds

SUV: 22 – 25 mph Pickup: 42 – 45 mph

Peak Impact Force

SUV: Fx= -37,850 lb, Fy= 66,350 lb, Fz= -1,560 lb Pickup: Fx= -71,400 lb, Fy= -26,700 lb, Fz= 1,880 lb

Peak Impact Acceleration

SUV: 18 – 23 g Pickup: 12 – 15 g

Peak Angular Velocity

SUV:

~ -340 deg/sec

PDOF

SUV:

~ -61 deg

Occupant Analysis (Right-Rear)

Seat Belt Status: Unbelted General Motion of Occupant: Left/Forward Facial Contact Detected: Left Front Seat Headrest

Required Technologies

SIMON 3-D Vehicle Dynamics Simulation Model DyMESH 3-D Collision Model GATB 3-D Occupant/Pedestrian Simulation Model

Other software may claim to answer these questions. Have you closely evaluated their claims and the basis for their results?

To learn more about HVE and all of the available tools for crash investigators, call 503.644.4500 or visit www.edccorp.com. Our professional sales staff will help you get started using HVE today!

Knowledge = Power 7096-0820

Engineering Dynamics Company, LLC • 550M Ritchie Hwy #128, Severna Park, MD 21146 USA • Phone 503.644.4500 • www.edccorp.com

BONUS MATERIAL https://spaces.hightail.com/space/HsD35EuCNL Access Code: Collision132# New to Collision Magazine is the Digital Download feature. Use the URL and Access Code above to gain access to a private webpage containing additional bonus material for this issue of Collision Magazine. We would like to thank MATAI and SATAI for sharing this valuable crash/research data with Collision Magazine subscribers.

MATAI 2019 Conference Crash Data

www.matai.org

Dave Hallman of Hallman Engineering and his team worked with MATAI to develop a series of crash tests for their 2019 annual conference. The theme was school bus crash investigation. This data can be used as an excellent training tool, allowing attendees to not only witness the crashes, but also allows for analytical evaluation and reconstruction of a crash. The team assembled by MATAI and Hallman collected a huge volume of data, photos and video which, of course, wouldn’t be practical to includes on the printed pages of Collision so they have made some of the data available to readers of Collision Magazine on the "Digital Download" web page.

SATAI 2019 Conference Crash Data

www.satai.com

Similarly, the SATAI held their 2019 Winter Conference in Glendale, AZ and there conducted a series of instrumented and documented crash tests. Bob Anderson led a team at the 2019 SATAI Crash Conference which featured six pre-conference, rear-end impact tests between a 2008 Mercury Grand Marquis and a 2006 Chevrolet Impala. The impact speeds were within the 6 to 12 mph range. During the conference, an additional set of 3 more higher speed crash tests were conducted using Mike Rosenfeld’s Phantom Driver system. Some of the data collected at this conference is also available to readers of Collision Magazine on the "Digital Download" web page.

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Collision Magazine - Volume 13 Issue 2 1

Capture. Draw. Present.

A common problem that many agencies face is how to make efficient use of all the data that can get captured at a scene. The last thing you need to worry about is how to use all this data to produce the reports and deliverables you need. We can help.

Read more, on page 63

Contents

Volume 13 Issue 2

inside 1

Digital Download - Bonus Material Access

4

Letter From the Editor

4

Collision Magazine Information

6

features 6

Vehicle Fire = No Electronic Data?

by Michael Stogsdill

22

Vehicle Speed From Sound

by Alan Moore

30

Crash Behaviour In A Crash Comparison: The New Biofidelic Dummy in Different Scenarios Of Accidents Involving Passenger Cars And Pedestrians

by Annika Kortman

42

Crash-ol-o-gy: Introducing Toyota Vehicle Control History

by Wesley Vandiver and Robert Anderson

48

Toyota Gen1 EDR Event Recording Logic

by W. R. Rusty Haight and Robert Anderson

68

Motorcycle Accident Reconstruction: Incorporating EDR Data from the Struck Vehicle

by Nathan Rose, Neal Carter, Martin Randolph and William Bortles

92

Tesla EDR Case Studies & Reconstruction Techniques

by Weston Brown and Robert Anderson

106

Identifying Infotainment Systems for Use in Accident Reconstruction

by Shawn Harrington

114

Guardrail Crash End Terminal Reconstruction and Analysis

by Lawrence Wilson and Dr. Kevin Schrum

30

92

114 XX

case problem 56

CASE PROBLEM: Toyota Gen1 EDR Event Recording Logic

by W. R. Rusty Haight and Robert Anderson

128

SOLUTION: Toyota Gen1 EDR Event Recording Logic

by W. R. Rusty Haight and Robert Anderson

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Volume 15 Issue 1.indd 112

5/16/2021 2:34:08 PM

Letter from the editor In this Issue of Collision, we are happy to offer what we hope will be a exciting and valuable new feature - "Digital Download". For years, various organizations and researchers have been conducting crash tests as part of conference or research packages. For example, the Case Problem in this issue was drawn from a series of crash tests conducted by Collision Safety Institute with the Texas Department of Public Safety Highway Patrol’s State Crash Reconstruction Team.

This issue of Collision Magazine features crash test data and research from the Midwest Association of Technical Accident Investigators (MATAI) and the Southwestern Association of Technical Accident Investigators (SATAI).

Digital Download Feature: See page 1 of this issue Dave Hallman of Hallman Engineering and his team worked with MATAI to develop a series of crash tests for their 2019 annual conference. The theme was school bus crash investigation. This data can be used as an excellent training tool, allowing attendees to not only witness the crashes, but also allows for analytical evaluation and reconstruction of a crash. The team assembled by MATAI and Hallman collected a huge volume of data, photos and video which, of course, wouldn’t be practical to includes on the printed pages of Collision so they have made some of the data available to readers of Collision Magazine on the "Digital Download" web page. Please go to page 1 of this issue for URL and Access Code.

Similarly, the SATAI held their 2019 Winter Conference in Glendale, Az and there conducted a series of instrumented and documented crash tests. Bob Anderson led a team at the 2019 SATAI Crash Conference which featured six pre-conference, rear-end impact tests between a 2008 Mercury Grand Marquis and a 2006 Chevrolet Impala. The impact speeds were within the 6 to 12 mph range. During the conference, an additional set of 3 more higher speed crash tests were conducted using Mike Rosenfeld’s Phantom Driver system. Some of the data collected at this conference is also available to readers of Collision Magazine on the "Digital Download" web page. Please go to page 1 of this issue for URL and Access Code.

Both MATAI and SATAI have a long history of providing quality training opportunities for their membership both as a function of their presentation program during their conferences and crash tests they have planned and carried out. We at Collision magazine cannot recommend strongly enough that our readers check out their web sites at: - http://matai.org/ - https://www.satai.com/ where you can find membership details and other useful links and information.

Scott Baker Managing Editor

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

COLLISION STAFF Scott Baker Sean Haight Tonya Baker

Owner, Managing Editor Senior Editor Advertising Manager

All rights reserved, © 2019 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.

SUBMIT AN ARTICLE If you would like to submit an article to be published in Collision please use the following link: https://www.collisionmagazine.com/submit/

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ADVERTISER INDEX

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4N6XPRT Systems 136-137 Collision Magazine 5 Collision Safety Institute 104 Crash Data Group Back Cover Crash Hub 90-91 EDR Summit 113 Engineering Dynamics Inside Front Forensic Mapping 47 Houston Auto Appraisers 29 Laser Technology Inc 41 Leica Geosystems 2, 63-66 Northwestern University 67 Tesla EDR Hardware 105

Vehicle Fire = No Electronic Data?

Michael Stogsdill

Collision Reconstruction Services, LLC

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A

bstract Many vehicles on the road today contain event data recorders which may record valuable data with respect to automobile collisions. In addition to the event data recorders, in-vehicle infotainment systems may store vehicle navigation information as well as cellular phone data from the vehicle's occupants. In addition to the electronic storage devices on the vehicle, occupants of the vehicle often bring their smart telephones, tablets, notebook computers, and other electronic devices in the vehicle. This paper will look at the electronic storage devices after they have been involved in a car fire as well as look at the temperatures of a car fire.

I

ntroduction According to the United States Fire Administration, one out of eight fires that fire fighters respond to are vehicle fires. Eighty-three percent of the vehicle fires involved passenger vehicles. Sixty percent of the fatal vehicle fires were the result of a collision (Topical Fire Report Series 2018).

Automobile manufacturers have implemented technology within vehicles to collect data in automobile collisions. The data is often written in event data recorders, which are subcomponents of vehicle modules such as an airbag control module, powertrain control module, roll-over sensor, pedestrian protection module, and/or an active safety control module. Other electronic data can also be stored in vehicle’s in-car entertainment or in-vehicle infotainment systems. Infotainment systems are comprised of hardware and software within the vehicle to allow the driver and passengers to have access to audio and video content, social networking, and games as well as navigation and vehicle diagnostics (Pati 2018).

In addition to electronic storage mediums within the vehicle, passengers in the vehicle will bring other electronic devices with them. These devices can include cellular phones, smart phones, tablets, notebooks, GPS units as well as gaming devices and other electronic devices. During a vehicle fire, these items will often be left behind and burned in the fire.

After the fire has been extinguished, and the smoke has cleared, can the data within the electronic devices be retrieved?

T

esting The car fires were conducted in Briggsdale Colorado with the assistance of the Briggsdale Fire Department. Four vehicles were burned. Each vehicle was instrumented with K-type thermometers and K-type thermocouples.

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The TekcoPlus Ltd K-type thermometers had a temperature range of -200 to 1370 degrees Celsius/ - 328 to 2498 degrees Fahrenheit. The resolution was 0.1 degrees C/F with an accuracy under 18-28 C ambient temp +/- 0.3% reading +1 degree C. The thermometers completed data logs of temperature samples.

length of 3 meters. Extensions to the thermocouples were made utilizing K-type thermocouple wires, AWG 24, along with K-type male and female connectors.

Figure 2: Thermocouples

Figure 1: Thermometer and data logger The thermocouples used were 3M K-type mini-connector.

The thermocouples had a range of -50 – 700 degrees Celsius. The probes were insulated with fiberglass and had a

“

After the fiFIre has been extinguished, and the smoke has cleared, can the data within the electronic devices be retrieved?

V

ehicles Four vehicles were obtained to burn: a 2007 Ford Focus, 2011 Ford Fusion, 2012 Ford Focus, and a 2005 Ford Taurus. Burns were started at different points of origin; the engine compartment, occupant compartment, trunk, and underneath the vehicle’s occupant compartment. All the vehicle fires were conducted under the supervision and direction of the Briggsdale Fire Department at their fire department.

Prior to each burn, photographs were taken of the vehicle’s exterior and interior. In addition to the vehicle’s electronic modules, additional portable electronic devices were placed in the vehicles.

Four thermocouples were utilized on each vehicle and placed in the engine compartment, at the top of the occupant compartment, at the driver’s steering wheel, and in the vehicle trunk.

2007 Ford Focus

Figure 3: 2007 Ford Focus 8

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The 2007 Ford Focus airbag control module was not supported by the Bosch Crash Data Retrieval System so the vehicle fire for this vehicle was set in the trunk. A notebook computer, two Casio cellular phones, and a tablet were placed in the vehicle (figure 4).

Figure 5: Fire at 1:17 PM

Figure 4: 2007 Ford Focus Trunk Contents A blend of diesel fuel and gasoline was poured in the trunk of the vehicle. The trunk lid was closed, but not latched.

The fire was ignited with a flare underneath the rear of the vehicle. The temperatures increased rapidly to over 1000 degrees Fahrenheit in approximately 4 minutes and remained over 1000 degrees F for approximately 2 minutes and then the fire started to smolder, and the temperatures dropped down to approximately 300 degrees Fahrenheit in approximately 5 minutes time. The trunk lid was opened, and the temperature increased as the fire received oxygen.

The temperature climbed up to approximately 670 degrees Fahrenheit in approximately 2 minutes and then spiked to temperatures over 1200 degrees Fahrenheit. The temperature in the trunk slowly dropped over approximately 5 minutes to readings of over 800 degrees Fahrenheit when the fire was extinguished by the firefighters. From the first ignition to the point when the fire department extinguished the fire, the total burn time was approximately 25 minutes.

Temperature readings were taken in the engine compartment, at the steering wheel, and the roof of the occupant compartment. The temperatures in the engine compartment did not rise above the ambient air temperatures. The occupant compartment temperatures did rise as the fire burned in the trunk. The steering wheel temperatures increased to over 600 degrees Fahrenheit in approximately 18 minutes. The temperatures at the roof of the occupant compartment rose quickly to approximately 375 degrees www.collisionpublishing.com

Figure 6: Fire at 1:24 PM

Figure 7: Fire at 1:30 PM

Figure 8: Extinguished at 1:32 PM Collision Magazine - Volume 13 Issue 2 9

in 3 minutes. The temperature did not increase until approximately 10 minutes into the burn. The temperatures rose rapidly to over 1300 degrees Fahrenheit after the fire burned for approximately 18 minutes.

Figure 10: Trunk contents after fire

Figure 11: Front seats after fire

Figure 12: Rear seats after fire Figure 9: Trunk Fire Time/Temperature Chart 10

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Figure 16: Chips in Casio Phone Figure 13: Engine compartment after fire Figures 10-13 show the damage to the vehicle after the fire was extinguished. The electronic items were removed from the trunk and packaged for transfer to VTO Labs, located in Broomfield, Colorado, to be analyzed.

At the lab, each item was disassembled to access the computer chip or hard drive. The memory chips from the Casio phones were damaged to the point that no data could be retrieved (Figures 14-17)

“

Figure 17: Destroyed chip on Casio phone

Figure 14: Casio phone

Figure 15: Casio Phone

At the lab, each item was disassembled to access the computer chip or hard drive.

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Figure 18: Samsung Tablet from trunk 2012 Ford Focus The 2012 Ford Focus had an airbag control module which was supported by the Bosch Crash Data Retrieval System.

The module was imaged prior to the vehicle fire. A Samsung phone was placed in the center console.

The fire was set in the engine compartment utilizing a mixture of diesel and gasoline. Unfortunately, the thermocouple in the engine compartment stopped sending data during the majority of the burn. The thermocouple in the trunk also failed to report values for portions of the burn.

The thermocouples in the occupant compartment did record temperature readings. The fire was started at 2:25 PM and for the first 9 minutes, there was a minor temperature increase in the occupant compartment and at the steering wheel of the vehicle. After approximately 9 minutes, the temperatures in the occupant compartment rose quickly and at approximately 13 minutes, the temperatures near the roof of the occupant compartment were over 1300 degrees Fahrenheit. The temperatures at the steering wheel increased a little slower, but eventually matched the temperature at the roof which was over 1400 degrees Fahr-

Figure 19: 2012 Ford Focus 12

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Figure 20: Fire at 2:28 PM

Figure 21: Fire at 2:44 PM

Figure 22: Fire at 2:47 PM

Figure 23: Extinguished at 2:49 PM www.collisionpublishing.com

Figure 24: 2012 Ford Focus Time and Temperature log Collision Magazine - Volume 13 Issue 2 13

enheit. After burning for approximately 25 minutes, the vehicle fire was put out.

Figures 25-28 show the damage to the vehicle after the fire was extinguished. The electronic items were removed from the vehicle and packaged for transfer to VTO Labs to be analyzed.

At VTO Labs, each item was disassembled to access the computer chip or hard drive. The airbag control module was damaged so that data could not be retrieved. The Samsung phone was also damaged so no data could be retrieved. (Figures 29-32) 2011 Ford Fusion The 2011 Ford Fusion was equipped with an airbag control module which was supported by the Bosch Crash Data Retrieval System. An image of the data contained in the airbag control module was done prior to the burn. The Fusion was also equipped with Ford’s Sync infotainment system. The infotainment module was removed from the vehicle and a baseline image was attempted utilizing the Berla iVe system. Unfortunately, an image of the infotainment data was not successful after three attempts.

14

Several electronic items were placed in the car. These items included an Android phone, two Casio flip phones, Android tablet, a GPS unit, a notebook computer, a tablet, and a smart phone.

The fire was set in the in the driver’s seat with a mixture of gasoline and diesel fuel at approximately 4:11 PM. The fuel burned quickly initially but failed to ignite the car’s interior. The driver’s door was opened by a firefighter approximately 5 minutes after the initial ignition. Upon opening the driver’s door, the firefighter found the fire had almost extinguished itself. The fire burned for a couple of minutes and then started to go out. The fire was reignited at approximately 4:24 PM. The temperatures at the steering wheel and the roof of the occupant compartment climbed rapidly upon the fire’s ignition. The temperature at the roof of the vehicle climbed up to 1000 degrees Fahrenheit within one minute and then continued to climb up to a peak temperature of 1902 degrees Fahrenheit. The temperature at the steering wheel climbed to a peak temperature of 1759 degrees Fahrenheit.

Figure 25: Trunk

Figure 26: Engine Compartment

Figure 27: Front seats

Figure 28: Occupant compartment

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Figure 29: Airbag control module in the 2012 Ford Focus

Figure 30: Airbag control module removed from the vehicle

Figure 31: Samsung phone

Figure 32: Chip from Samsung phone

Figure 33: 2011 Ford Fusion

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Figure 34: Fire at 4:12 PM

Figure 35: Fire at 4:31 PM

Figure 36: Fire at 4:40 PM

Figure 38: Ford Fusion Time and temperature log

Figure 37: Fire at suppression 16

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After the fire was extinguished, the electronic devices were retrieved from the vehicle and later taken to VTO Labs for analysis. The airbag control module was damaged in the fire and data was not retrieved from the module’s event data recorder. The infotainment module was damaged, and no data could be retrieved from the infotainment module. The cellular phones, laptop computer, and the GPS unit were all damaged and no data was retrieved.

Figure 42: Occupant compartment Ford Fusion

Figure 39: Ford Fusion Engine Compartment Figure 43: Samsung Tablet from Ford Fusion

Figure 40: Occupant compartment Ford Fusion Figure 44: Board from Samsung Tablet

Figure 41: Ford Fusion Trunk

Figure 45: GPS unit from dash of Ford Fusion

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Figure 46: Hard disc drive from notebook computer

Figure 47: Airbag Control Module from Ford Fusion

Figure 48: Airbag Control Module from Ford Fusion

Figure 49: ACM circuit board 18

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Figure 50: ACM circuit board and inside of case

Figure 51: Phone on passenger floor of Ford Fusion

Figure 52: Phone circuit board from passenger floor of Ford Fusion

Figure 53: Infotainment module from Ford Fusion www.collisionpublishing.com

The vehicle was ignited at approximately 2:13 PM. The fire was started underneath the driver’s seat outside of the vehicle. The fire burned up from under the car and into the vehicle.

Figure 54: Infotainment module from Ford Fusion Figure 56: Thermocouple placement at EEPROM

2005 Ford Taurus

Figure 55: 2005 Ford Taurus On February 2, 2019, a 2005 Ford Taurus was burned.

The 2005 Ford Taurus was equipped with airbag control module with airbag control module which was not supported by the Bosch Crash Data Retrieval System. Four modules were placed in the vehicle on the center tunnel.

The four modules were had two thermocouples, one inside of the module case, the other on the outside of the case.

The vehicle was equipped with four thermocouples, one in the trunk, the steering wheel, the roof of the occupant compartment, and one in the engine compartment.

Figure 57: Module placement The temperature in the vehicle did not increase until approximately 8 minutes after the fire was started. The temperature in the occupant compartment rose for approximately 4 minutes, peaking at 1275 degrees Fahrenheit.

The temperature at the steering wheel rose up to a peak of 276 degrees approximately 11 minutes after the fire was started. (Figure 58)

The airbag control modules which were placed in the vehicle were from a Chevrolet Cobalt, a Chevrolet Cavalier, a Chevrolet Impala, and a Toyota Sienna. All four of the modules were imaged utilizing the Bosch Crash Data Retrieval system.

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Figure 58: Ford Taurus Time and Temperature Chart

Figure 59: Temperature inside modules and cases

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Figure 60: Cavalier and Cobalt module temperatures The temperature around the modules was monitored at the base of each module. Each module also had a thermocouple inside the case. The temperature on the outside of the Toyota module, which was the closest to the firewall, peaked at 433 degrees Fahrenheit at approximately 2:25 PM, which was approximately 12 minutes after the fire was started. Inside of the Toyota module, the temperature peaked at 191 degrees Fahrenheit at 2:26 PM. (Figure 59) The outside of the Impala module, which was then next in line from the front of the vehicle, peaked at 268 degrees at 2:29 PM, which was approximately 16 minutes after the fire was started. The peak temperature inside of the Impala module was 154 degrees Fahrenheit at approximately 2:27 PM, which was approximately 14 minutes after the start of the fire. (Figure 59) The temperature on the outside of the Cavalier module, which was the third back from the firewall, peaked at 181 degrees Fahrenheit at approximately 2:26 PM, which was approximately 13 minutes after the fire was started. Inside of the Cavalier module, the temperature peaked at 127 degrees Fahrenheit at 2:27 PM. (Figure 60) The outside of the Cobalt module, which was the last in line from the front of the vehicle, peaked at 124 degrees at 2:28 PM, which was approximately 15 minutes after the fire was started. The peak temperature inside of the Cobalt module was 426 degrees Fahrenheit at approximately 2:25 PM, which was approximately 12 minutes after the start of the fire. (Figure 60)

After the fire was extinguished, the modules were collected.

Each of the four modules were subjected to water which the fire department used to put out the flames. The modules were allowed to dry thoroughly before imaging was conducted. Three modules were imaged after the fire; the Cobalt module, Cavalier module, and the Impala module.

The pre-burn report for each module which was generated by the Bosch Crash Data Retrieval software compared to the post-burn report generated by the Bosch Crash Data Retrieval software. The two reports were identical, except for the ignition cycles. There was no alteration of the data as a result of the modules being exposed to the heat of the vehicle fire or the water which the modules were exposed to during the extinguishing of the fire.

B

ibliography “Highway Vehicle Fires,” (2018) Topical Fire Report Series, Volume 19, Issue 2, retrieved from

https://www.usfa.fema.gov/downloads/pdf/statistics/v19i2.pdf Pati, Sambhar, (2018) “Everything You Need to Know About In-Vehicle Infotainment Systems”, www.einfochips.com, Retrieved from https://www.einfochips.com/blog/everything-you-need-to-know-about-invehicle-infotainment-system/

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Vehicle speed from Sound Alan Moore, P.E., ACTAR

V

ideo recordings are frequently available as useful evidence in an accident reconstruction. Not as often, a video recording may contain the sound of a vehicle but not useful video images. This could occur in a motorcycle collision where the motorcycle strikes the side of the vehicle equipped with a dashcam, such that the motorcycle is not visible in the video prior to impact. It could also occur if a dashcam was able to view a passing motorcycle, but the video resolution and frame rate were insufficient to calculate the motorcycle’s speed.

Also, a dashcam may record engine sound but the video quality is insufficient for using visual references to calculate speed. In such cases, the audio recording of the vehicle’s engine can be used to calculate its speed.

Two methods were evaluated. The first is to use Doppler shift alone to evaluate speed. The second is to use spectral analysis of the engine sound, along with vehicle specifications, to determine vehicle speed from engine speed.

The Doppler effect is generally expressed as the following:

Where: is the velocity of waves in the medium is the velocity of the receiver relative to the medium (positive if the receiver is moving towards the source) is the velocity of the source relative to the medium (positive if the source is moving away from the receiver) The general form of the equation, when adapted for accident reconstruction use, is: ...

Doppler shift method Austrian physicist Christian Doppler first reported a phenomenon in 1842 wherein the frequency of a waveform measured by an observer is dependent on the relative speed between the transmitting source and the observer. In the most simple scenario wherein the transmitting source is moving directly toward the observer, the movement of the transmitter between each waveform produces the effect of compressing the waveforms in time in front of the transmitter and expanding them behind the transmitter – thereby producing a change in frequency. As a result, the approaching sound of a siren is of a higher pitch than as it moves away.

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Where: ...

The frequency plot is generated from the audio track of the video using FFT (Fast Fourier Transform). Several free programs are available to generate FFTs; examples in this paper were mostly produced with Audizr for Windows.

Using the Doppler shift method, any identifiable peak in the vehicle sound can be used, such as engine, gear or tire

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noise, and any distinctive component of wind noise may also be used.

An FFT can be demonstrated using a musical instrument.

“Concert A” is a standard pitch used for tuning musical instruments, and is at 440 Hz. Played on a guitar, its FFT is as shown in Figure 1a.

As an example, a motorcycle was driven past a stationary camera at a GPS-measured speed varying between 39.1 and 39.5 mph. Figure 1b shows the frequency plot of the recording; the approach of the motorcycle is shown in red, its departure past the camera in yellow. Any prominent peak in the data can generally be used. In this case, the prominent engine sound was used. The peak was 120 Hz on the approach, and 108 Hz after it passed the camera.

Using the above equation, the calculated speed is 40.4 mph, as shown below: ...

Where:

Figure 1a: “Concert A” at 440 Hz on a guitar

108 Hz 120 Hz

Figure 1b: Motorcycle approach frequency in red, and departure frequency in yellow.

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The Doppler shift is related not to the ground speed of the noise source, but to the relative speed between the observer (i.e., the video camera) and the noise source. So if the camera is moving, its speed must be determined. The most common moving camera source is a dashcam, where its host vehicle speed may be measured by GPS reported by the dashcam, or by conventional video analysis. If the camera vehicle has a significant speed on a path in a different direction than the noise source, the analysis is considerably more complex and is not considered in this paper.

Consider a scenario where the camera vehicle runs a stop sign and strikes a motorcycle which is approaching on a perpendicular path. The Doppler shift is influenced by the forward travel of the camera, and requires a more detailed analysis. However, if the camera vehicle is making a slow left turn across the path of the noise source, the angled travel will have a negligible effect on Doppler shift.

Engine speed by spectral analysis method In the second method, the engine RPM is determined by spectral analysis, and used to calculate vehicle speed through gear ratios and tire size. The engine RPM is indicated by its firing frequency.

Each combustion event, when released by the exhaust valve opening, creates a sound pulse. On a four-stroke engine this occurs every other revolution for a single cylinder. On a four cylinder engine the combustion frequency is four times higher, and is therefore twice per revolution. On an eight cylinder engine, the combustion frequency is four times per revolution. A four cylinder engine’s combus-

tion frequency, or firing frequency, is then a second-order event; it occurs twice as often as the engine rotation. A six cylinder is third order and an eight cylinder is fourth order.

The engine firing frequency is often the lowest pitch, and largest amplitude, of the identifiable peaks. Multiples of that frequency may be visible as harmonics, and are not usable unless the multiple is known.

In the example shown in Figure 2, a four-cylinder engine running at 3,000 RPM produces a firing frequency of 100 Hz. Since the frequency is the source parameter, it is used to derive the engine speed. 100 Hz, when multiplied by 60, is equivalent to 6,000 combustion events per minute.

On the four cylinder example, each cylinder produces onefourth of the events, or 1,500 events per minute. Since two revolutions are required per combustion event on a four-stroke engine, doubling the 1,500 results in an engine speed of 3,000 RPM. Note in Figure 2 that numerous harmonics and other excitations are visible; the largest and lowest frequency is generally the firing frequency.

Vehicles that have a prominent engine sound, such as motorcycles and heavy trucks, are also likely to have distinct resonances, since NVH (Noise, Vibration and Harshness) is not as well tuned as it is in passenger vehicles. Such resonances can falsely appear to be peaks of the engine’s firing frequency. Figure 3 is an example of this where the firing frequency is 365 Hertz, but there is a larger and lower peak at approximately 180 Hz. The lower 180 Hz peak is a resonance of a part of the motorcycle, and is unrelated to the engine speed. To further investigate the resonance, and

100 Hz

Figure 2: A four-cylinder engine running at 3,000 RPM, exhibiting a firing frequency of 100 Hz on an FFT plot 24

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Figure 3: A four-cylinder engine operating at approximately 10,000 RPM The firing frequency is 365 Hz. A larger peak at 180 Hz is an unrelated resonance within the motorcycle identify it separately from the engine firing frequency, a spectrogram was used. A spectrogram is a graph of sound, with lower frequencies at the bottom and high frequency at the top. It moves through time from left to right. Loudness is indicated by brighter colors, and lower noise levels are indicated by darker colors.

The spectrogram in Figure 4 is an example of this phenomenon; the engine’s firing frequency is shown. It is close, in

frequency, to a resonance at approximately 180 Hz. As a result, it excites and amplifies that resonance. When the engine slows and the firing frequency aligns with the resonance, the amplitude increases significantly. Yet the resonance frequency remains constant, and does not vary with engine speed. So, the correct peak frequency to use is the engine’s firing frequency at 365 Hz, not the unrelated resonant frequency at 180 Hz.

Decelerating engine overlaps the 180Hz resonance, causing a higher sound level

Engine firing frequency

180 Hz resonance, unrelated to engine speed

Figure 4: Spectrogram of a motorcycle’s firing frequency and an unrelated resonant frequency at 180 Hz. www.collisionpublishing.com

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Once the firing frequency is determined, the below equation can be used to calculate vehicle speed, as long as no Doppler effects apply. The most direct application is when the video camera is on the vehicle in question.

The axle, or final drive ratio, of a motorcycle can easily be modified by the end user and should be verified, especially on sportbikes, by recording the number of teeth on the front and rear sprockets for comparison to published specifications. The tire size, specified in revolutions per mile, can be obtained from published specifications. Particularly in motorcycle cases, where tires of the same size but different model can vary in circumference, the rolling circumference can be directly measured on the involved or an exemplar motorcycle.

The data presented in Figure 3 is from a 2005 BMW K1200S in use at Daytona Speedway. With a priori knowledge that the motorcycle was in fourth gear, Equation (2) and the 365 Hz firing frequency produces a speed of 142.5 mph. 20 Hz GPS data measured the speed as 140.9 mph (Figure 5), suggesting an excellent fit, especially considering that the audio contains considerable wind and other extraneous noise.

In some cases, the motorcycle’s gear position can be determined from a post-crash inspection. If a 10-speed heavy truck is being evaluated, an assumption of top gear operation may be applicable if the likely road speed is above 55 mph. In other cases the gear position is not known, and only a range of possible speeds can be produced. Table 1 shows the possible speeds resulting in the Daytona example. By observation of other motorcycles in the same operating environment, gears 1,2,3 and 6 could reasonably be excluded, which leaves two possible speeds of 142.5 mph and 160.4 mph. In this example no further evidence (other than the independent GPS measurement) is available to discern the correct gear position.

Gear 1 2 3 4 5 6

Speed, mph 72.7 99.6 126.0 ? 142.5 ? 160.4 180.7

Table 1: Possible gear positions of the motorcycle

Figure 5: GPS data showing speed at 140.9 mph 26

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In regards to evaluating gear position, there is an additional source of information. If the motorcycle sustained sufficient damage to set an engine fault code, and is configured to record freeze frame or snapshot data associated with the fault code, it may be used to find gear position. Since there is usually a delay between the engine damage and recording of the fault code and associated data, such data rarely indicates the vehicle pre-impact speed. However, if the engine and speed sensor are still operating when the fault is recorded, then the data can be used to calculate gear position. Transmission ratios can be obtained from published specifications, or exemplar tests can be conducted. Figure 6 indicates the snapshot data recorded in conjunction with an intentionally generated fault code on a 2009 Honda CBR600 RR. There was approximately 9 seconds of delay

between generation of the physical fault and recording of the fault code and snapshot data in the engine ECM, so the recorded speed was irrelevant to the pre-impact speed.

However, the engine and countershaft sprocket (for the drive chain) continued to rotate and so a gear ratio can be evaluated. The ratio of RPM to mph from the snapshot is 1469 RPM divided by 9 mph, or 163.

The exemplar motorcycle was run through all six gears, and the engine speed and vehicle speed were recorded.

A plot of the data was prepared (Figure 7), and the gear position corresponding to a ratio of 163 was found. The Honda motorcycle was in second gear when the fault was generated.

Figure 6: Snapshot data from a fault code on a 2009 Honda CBR600 RR motorcycle

Figure 7: Relationship of engine RPM and vehicle speed in mph, for each gear www.collisionpublishing.com

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Braking Vehicle sound does not allow determination of braking, unless an unusual mechanical braking noise occurs or tire skidding is audible. However, the vehicle sound can be used to determine when the driver released the throttle.

In particular, the transition between leading and trailing throttle can be identified.

Leading throttle occurs when the engine is producing torque and the drivetrain is under load. Neutral throttle is a point where the engine is providing neither torque or engine braking, and the drivetrain components are unloaded.

Trailing throttle occurs when the throttle is released, and engine compression is slowing the vehicle, causing drivetrain loading in the opposite direction of leading throttle.

In some vehicles the transition from leading to trailing throttle, or vice versa, can be identified by analysis of gear noise. Polygon Bearings- Reducing Resonant Vibration in Automotive Differentials, INA Wälzlager Schaeffler oHG, 2000, has a good description of the difference in gear noise. Figure 8 is an example of gear noise under leading throttle, with a small peak at 425 Hz. After the driver released the throttle, causing a transition to trailing throttle, the peak moved to a lower frequency of 375 Hz (Figure 9).

425 Hz

Figure 8: Peak at 425 hz with leading throttle

375 Hz

Figure 9: Peak at 375 hz with trailing throttle 28

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are small, but at high altitude they can be significant.

In such cases the 767.3 mph constant for speed of sound should be adjusted.

Audio sample rate Video sources often relied upon in accident reconstruction may not have consistent frame rates, and may exhibit duplicate frames that must be considered in performing a speed evaluation from video. Because the audio is recorded as a separate track at a separate sampling rate, it is not necessarily affected by a video frame rate error. The sample rate of an audio track is several orders higher than the video frame rate. As an example, a dashcam video recorded at 10 frames per second may contain an audio track recorded at 16 kHz. Since the frequency content generally of interest is 30-500 Hz, it is well within the frequency response of even a low sample rate audio recording.

Limitations of the Methods Application of the 2 methods described should involve consideration of the following limitations: •

Elevation - The speed of sound changes with altitude, temperature and humidity. Near sea level the effects

•

Occupant conversation – With a dashcam inside the passenger compartment, any recording of occupants voices may mask the audio signature of the vehicle in question.

•

Torque converter slip – using the spectral analysis method requires knowledge of the overall gear ratio. If a torque converter is present, and the lockup clutch is not engaged, the converter’s slip presents an unknown value that either requires estimation or impedes use of the spectral analysis method.

•

Uncertainty on tire size – consider using both the published unloaded and loaded radius. If possible, mark the subject tire on the subject vehicle, roll it several revolutions under the pre-crash loading, and measure the rolling circumference.

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CRASH BEHAVIOUR IN A CRASH COMPARISON:

THE NEW BIOFIDELIC DUMMY IN DIFFERENT SCENARIOS OF ACCIDENTS INVOLVING PASSENGER CARS AND PEDESTRIANS Annika Kortmann

I

n the reconstruction of passenger car-pedestrian accidents, the vehicle damages and the pedestrians injuries are important indications, which can now be realistically reproduced by means of the new biofidelic dummy. A series of selected crash tests clarifies the differences between the dummies including during the lateral impact with a normal passenger car front and an SUV. Also included in this article are collisions with the head-on impact of a dummy which is facing in the direction of the car, a grazing contact with the windows and the A-pillar.

The dummies previously used for such crash tests usually have a “bone structure”, which mainly consists of aluminium and steel, so that the extremely hard construction of the dummy causes much greater damages to the vehicle than in real pedestrian accidents at the same collision speed. The same damage characteristics caused by a conventional dummy suggests a lower collision speed as that in a real accident.

and development of the so-called biofidelic dummy from Dr. Michael Weyde and have since July 2017 their own manufacturing laboratory. Due to the special construction, the biofidelic dummy has a very good comparability with the real human body. The used materials are selected according to their physical properties in order to reproduce the human “body parts” as precisely as possible. The “bones” of the dummy are, for example, made of epoxy resin and an admixture of aluminium powder in order to be able to reproduce the breaking resistance of human bones as realistically as possible. Also ligaments and tendons in the form of polypropylene straps form part of the biofidelic dummy. The reproduction of the soft tissues is accomplished by silicone and acrylic. Since every dummy is assembled by hand, step for step (see Figure 1 sidebar), it is also possible to deviate from the standard construction of the dummy with a height of 1,75 m and a weight of 79 kg to make custom-made models in terms of height and weight. The production time of a standard biofidelic dummy is approximately 2 weeks, custom-made products can be achieved in around 4 weeks. An example illustrating each stage of the production process is shown in Figure 2.

Due to the in house production by CTS, the dummy can be equipped with measuring technology during the finishing procedure, so that collision induced accelerations and forces occurring in the area of the cervical spine can be measured. By installing special sensors, the pressure can be measured which impacts, inter alia, areas such as the chest or individual segments of the spine during the collision.

The biofidelic dummy as a real replacement

Crash behaviour in crash comparison

Since the beginning of 2017, the company crashtest-service.com GmbH (CTS) has, in cooperation with the HTW Dresden and the TU Berlin, taken over the construction

In order to make the differences in the crash behaviour of the conventional dummy in steel-construction compared with the biofidelic dummy visible, crash tests were carried

Introduction Pedestrian accidents require a detailed reconstruction of the accident happenings due to the severe injury consequences often caused to the unprotected road traffic user.

For this reason, in modern accident reconstruction crash tests in which dummies are used are increasingly resorted to. In particularly important for limiting the collision velocity of the passenger car are, inter alia, the vehicle damages caused.

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out under different impact mechanisms of the vehicle against the dummy and then subsequently visually compared. Examined was, amongst others, a completely overlapped lateral impact of a pedestrian with a normal vehicle front (test vehicle: VW Golf III) and an SUV front facia (test vehicle: VW Touareg).

For the comparability, the same test vehicle was used after relevant repairs under the same impact configuration impacting a biofidelic and a conventional dummy (in this case a so-called NAMI-dummy 1).

Lateral impact of a normal passenger car front The Federal Statistical Office prepared a statistic of collisions between vehicles and pedestrians from the year 2013 2. From a total of 28,805 accidents, 95 % of those accidents happened in urban areas and only 5 % in suburban areas. For this reason, for the first crash test comparison between a biofidelic and NAMI-dummy, a scenario was chosen in which a VW Golf III collided head on with an urban-usual speed of 50 km/h simultaneously with the two dummies. Figure 3 shows the impact configuration with a conventional (NAMI-) dummy in vehicle plan view on the left side. The biofidelic dummy is on the right side. The differences in the movement sequences of the dummies during the collision, in particularly in the carryingphase, were filmed separately and are shown in Figure 4 and Figure 5. Figure 4 shows the movement sequences of the conventional dummy. Immediately after the impact, the legs of the dummy raise from the ground, a pedestrian accidents’ typical “undergoing” of the legs does not take place. Due to the rigid construction of the NAMI-dummy, the dummy does not cling to the car bonnet and collides in an almost stretched out position with the head against the windshield.

In the last sequence in Figure 4 it is clear to see that the body of the NAMI during the impact of the head has a significant clearance to the bonnet. The movement sequences of the biofidelic dummy by the impact of a passenger car (Figure 5) resembles the impact behaviour of a real pedestrian, by which the standing leg is pulled under the passenger car and the dummy clings to the bonnet during www.collisionpublishing.com

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Figure 2: Each stage of the biofidelic dummy production process in the damages caused to the bonnet, see Figure 8 (right image). During the impact of the NAMI on the car bonnet, the damages caused are extensive and also include several scratches, which would not occur in a real pedestrian accident (Figure 8, left image).

Lateral impact of an SUV-front

Figure 3: The impact configuration with a conventional (NAMI-) dummy in vehicle plan view on the left side the course of the collision. The head impact occurs with a movement from the top downwards whilst the body makes contact with the bonnet.

Also the comparison of the damages on the left and right side of the passenger car after the collision gives a much more realistic accident representation through the use of the biofidelic dummy. Figure 6 shows a top view of the vehicle front of the VW Golf III after the collision: left side; the damage pattern caused by the NAMI, right side; the damage pattern caused by the biofidelic dummy. It clearly shows that the passenger car in the contact area with the NAMI is much more severely damaged. Also the fracture pattern in the windscreen is diffused on the left side and the detail view in Figure 7 shows clearly that the NAMI, in comparison to the biofidelic dummy, actually penetrates the windscreen with its head. As a result of the clinging of the biofidelic dummies body during the collision, the contact of the hips and shoulder can be recognised afterwards 32

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Since the SUV has seen an ever growing popularity in the last 10 years and this vehicle type has meanwhile become a city-car [3], the primary existing lateral impact of a pedestrian, the damages caused to the passenger car and the movement sequences of the dummy during and after the collision with such a vehicle type have likewise been investigated.

Figure 9 shows the test setup, where a biofidelic and a NAMI-dummy under identical conditions are impacted with a VW Touareg (Type 7L) at a velocity of just over 40 kph. Also the comparison of the impact configurations shown in Figure 10 validates the identical test setup. By means of the optical comparison of the impact configurations from Figure 3 and Figure 10 it becomes evident, that the lower edge of the bonnet of the VW Touareg around is 25 cm higher than the VW Golf III. In order to determine whether the previous rolling-off and clinging-on behaviour of the biofidelic dummy to the bonnet also takes place with an SUV front, the study of the movement sequences during the collision is of crucial importance.

As also previously in the crash test with the VW Golf III, a high-speed recording was made from the side view taken at the height of the collision, the movement sequences are correspondingly shown in Figure 11 and 12. In Figure 11 is becomes apparent that despite of the higher bonnet, as previously observed, the clinging of the biofidelic dummy on the SUV front and bonnet also takes place. The

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Figure 4: The movement sequences of the conventional dummy

Figure 5: The movement sequences of the biofidelic dummy impacted by a passenger car (mirrored)

Figure 6: A top view of the vehicle front of the VW Golf III after the collision

Figure 7: Fracture pattern in the windscreen

Figure 8: Damages caused to the bonnet www.collisionpublishing.com

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Figure 9: The test setup, where a biofidelic and a NAMI-dummy under identical conditions are impacted with a VW Touareg

Figure 10: The test setup, where a biofidelic and a NAMI-dummy under identical conditions are impacted with a VW Touareg

Figure 11: Movement sequences during the test with the biofidelic dummy

Figure 12: Movement sequences during the test with the NAMI-dummy

Figure 13: End positions of the different dummy types 34

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typical undergoing of the feet is also present in the crash with this vehicle type. During the course of the collision, the dummy adapts itself with the contour of the vehicle front,whereby the upper body presses from the top on the bonnet and the head impacts the bonnet with such a momentum in a sideways tilting movement. After the main exchange of force the movement of the dummy is opposed and it is then thrown off forwards in the SUVs direction of travel.

Comparing the previous movement sequences of the biofidelic dummy with the NAMI-dummy based on the images series in Figure 12, it becomes clear how immobile the conventional dummy is in comparison to the biofidelic dummy. The absent deformation behaviour of the NAMIdummy means that the main force exchange is not on the top side of the bonnet but instead on the front edge. Thus, a head impact does not occur on the upper side of the bonnet. Rather, the rigid construction of the NAMI-dummy shows that it rotates on to its back during the collision and is therefore repulsed laterally from the vehicle after the main force exchange. An undergoing also does not occur, since the dummy takes on the speed of the SUV significantly quickly.

The end positions of the different dummy types reflect the significant deviation in the movement behaviour. Whilst the biofidelic dummy comes to rest after approximately 18 m almost straight in front of the SUV (Figure 13, right image), the NAMI-dummy rolled off the bonnet to the right during the collision due to the lack of deformation behaviour and reached a longitudinal throwing distance of only 14 m with a lateral offset of 2.5 m from the collision position (Figure 13, left image).

The comparison of the vehicle damages on the VW Touareg in both crash tests are shown in Figures 14 to 16.

It becomes clear that the SUV during the crash with the biofidelic dummy has a deep extensive indent in the bonnet, which results on the bonnet from the impact of the upper body and the top of the head. The force exchange takes place essentially from the top. At the point of impact with the NAMI-dummy the bonnet was impacted from the front so that the bonnet at the first contact was significantly compressed backwards and the paintwork partially flaked. Additionally, the front grill and the bumper covering underneath the right headlight have been damaged by the impact with the NAMI-dummy, which do not occur when impacting the biofidelic dummy.

Head-on impact with a normal passenger car front Usually in car-pedestrian collisions the pedestrian is impacting laterally, since the collision often occurs when

crossing the road. From a summary of many accident reconstruction expert reports relating to passenger car-pedestrian collisions it becomes clear that there are also cases in which a pedestrian is impacted head-on, usually with the intention to stop the vehicle.

As shown in the comparison in the series of images in Figure 17 the carrying-characteristic of the pedestrian is dependant on the impact direction. A lateral impact impedes the bending movement of the upper body, a head-on impact causes the pedestrian to bend like a folding knife on the vehicles front, the forwards bending of the upper body is unrestrictedly possible.

So far there are only very few crash tests in order to judge the vehicle damages during a head-on impact with a pedestrian. For the purpose of the preparing an accident reconstruction expert report, a crash test at CTS was commissioned, where a biofidelic dummy should be impacted head on at a speed of almost 60 kph. The test vehicle used was a VW Bora. In order to also be able to make a comparison in head-on collisions between a biofidelic and a conventional dummy a crash test with a Fiat Bravo from the CTS-database was also used, where a conventional dummy was impacted head-on at the same velocity. The comparison of the impact configurations is presented in Figure 18.

The movement sequence of the biofidelic dummy in a head-on crash is indicated in Figure 19. The upper body tilts forwards whilst the legs are initially pulled under the car and then thrown back (from the perspective of the pedestrian) by the vehicles front. At the collision speed of the VW Bora at 62 kph the dummy does not hit the bonnet, which is the case when laterally impacted and the collisionrelated rolling off, but rather hits the front windscreen of the test vehicle directly with the head.

The comparison of the vehicle damages is shown in Figure 20. The bonnet of the Fiat Bravo is severely deformed and raised, since the rigid Winterthur-dummy 1 pushes the bonnet back and inwards at the point of contact. A similar behaviour shown by the NAMI-dummy due to its immobility when laterally impacted by a VW Touareg.

Besides the dent in the front edge of the bonnet which can be attributed to the knee impact of the biofidelic dummy, the bonnet of the VW Bora at the same collision speed was not damaged, Figure 21. The fracture caused by the impact of the head is situated at roughly the same height as on the Fiat Bravo and shows a lower intensity. The fact that the front windscreen of the VW Bora came around two thirds loose from the frame and was pushed inwards it is possible that it is not solely due to the high collision speed of 62 kph. Since the original windscreen of the VW Bora was

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Figures 14-16: Vehicle damages to the VW Touareg slightly crasked before the crash test, it was replaced two days prior to the test. The unusual fracture pattern at the edges of the front windscreen is eventually due to the prior exchanging of the windscreen so that only the damages to the bonnet can be used to compare the crash behaviour of the biofidelic and conventional dummies. The unexpected minimal damages caused to the bonnet of the VW Bora in contrast to the severely deformed bonnet of the Fiat Bravo at the same velocity level rises the question whether in the past the collision speed of the accident vehicle in head-on 36

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collisions with pedestrians was partially underestimated based on the material provided and taking into account the damages of the vehicle.

Sliding collision of pedestrians with contact on the front windscreen and A-pillar Due to the massive construction of the conventional dummy, collisions in particular with pedestrians are of great importance, in which the dummy impacts the A-Pillar or

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Figure 18: The comparison of the impact configurations

Figure 17: Carrying-characteristic of the pedestrian

Figure 19: The movement sequence of the biofidelic dummy in a head-on crash impact configurations

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Figure 20: The comparison of the vehicle damages

Figure 21: The bonnet of the VW Bora the rood edge of the passenger car during the course of the collision.

Figure 22 shows hereby a crash comparison of a biofidelic dummy and a conventional dummy in a grazing impact by which it came to an impact with the head on the front windshield and subsequently a contact with the A-pillar.

On the left of Figure 22 shows a collision of an Audi Q7 at 57 kph with a biofidelic dummy. The right image shows a test by which a VW Golf III grazes a Hybrid-I dummy at 62 kph. The damage pattern comparison can be seen in Figure 23, which shows in both tests a significant damage to the front windscreen in the form of a fracture. The damage to the A-pillar fundamentally differentiates due to the vehicles and in particular the movement behaviour of the dummy.

Due to the mobility of the biofidelic dummy it hits the front windshield with the head in a nodding-movement and rolls then, initiated by the previous grazing impulse, over the A-pillar and the wing mirror. Due to the movement and therefore the change in contact points the A38

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pillar of the Audi does not appear to be deformed. In a collision with the VW Golf III, the dummy tilts over on to the vehicle. Because of the hard construction, the A-pillar is massively deformed.

Impact roof edge In particular, at higher collision velocities, where the dummies contact the roof edge of the vehicle, the most commonly used NAMI- or Hybrid II-dummy cause significantly more damages in contrast to an impact in the same area with a human body. For this reason, the estimation of the collision speed in accident reconstruction is made difficult.

Injury characteristics of the pedestrian Besides the damages caused to the passenger car, the use of the biofidelic dummy makes it now also possible to compare the injuries of the pedestrian in a real accident with those caused to the dummy. Through the addition of aluminium powder in the epoxy it is possible to x-ray the biofidelic dummy after the collision in order to see any

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Figure 22: Collision between Audi Q7 and VW Golf III with a biofidelic dummy

Figure 23: Damage pattern comparison fractures or breaks resulting from the crash test. This principal is presented in Figure 24 in form of a CT-scan of the predecessor model of the biofidelic dummy. In this case, the collision velocity at impact with the dummy was approximately 70 kph. The x-ray examination can be carried out after the crash test in cooperation with the veterinary clinic in Telgte, who obtained an official authorisation from the district government for undertaking such examinations. If required, an autopsy of the biofidelic dummy can also be done after the collision, an example is shown in the images of Figure 25. After carrying out crash tests for the reconstruction of passenger car-pedestrian accidents, it is possible for the biofidelic dummy to be repaired by CTS, so that the cost of the dummy for the crash test is only limited to the rental fee and therefore the dummy must not be completely charged for.

Conclusion In modern accident reconstruction the biofidelic dummy is a great benefit in order to limit the collision speed based

on the damage characteristics of the passenger car. With the construction design of the biofidelic dummy and the physical properties that resemble those of the human it is possible to also realistically recreate the injuries of a pedestrian.

By considering the available crash tests with biofidelic dummies it becomes in particularly clear in head-on pedestrian collisions that the damage extent caused to the crash vehicle at the same collision speed is much lower than in a collision with a conventional dummy. The question arises whether in the past the collision velocity of the accident vehicles based on the vehicle damages was partly underestimated.

The use of various types of measuring technology also offers extensive possibilities to record forces acting on the body, acceleration and pressures. The biofidelic dummy is constantly being further developed, so that, for example, the mobility is continuously improved and at the beginning of 2018 the dummy also received (among others) a

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new face with a bone structure made of epoxy. For accident reconstruction is it thus preferable that the biofidelic dummy be used in order to create sound expert reports based on comprehensive and visual documentation of damage patterns to the passenger car and the dummy as well as any measurement data obtained.

References 1. Technical data oft he used dummies are obtainable at crashtest-service.com GmbH 2. Statistisches Bundesamt, Verkehrsunfälle 2013, 7/2014.

3. Augsburger Allgemeine, „Warum Menschen SUVs kaufen“, 14. Juli 2017.

4. Dr. Michael Weyde, Ingenieurbüro Priester & Weyde, Heinrichstr. 5-6,12207 Berlin.

Author Dipl.-Phys. Annika Kortmann is a road traffic accident expert at the engineering office S+B in Münster (Germany).

Figure 24: CT-scan of the predecessor model of the biofidelic dummy

Figure 25: An autopsy of the biofidelic dummy after a collision

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

Robert Anderson

Collision Forensics, Inc.

Biomechanics Analysis

Introducing Toyota Vehicle Control History

E

vent Data Recorders (EDRs) have become a common source of important data for crash investigators. Since their introduction in the 1990s, EDRs have matured from devices that capture and record partial crash pulse data to sophisticated modules that can report a vast number of pre-crash and post-crash parameters. The important consideration here is the reference to a crash – “precrash” and “post-crash.” These devices are designed to capture data relative to a crash, allowing investigators to retrieve information that may assist them in reconstructing the incident.

The adoption of 49 CFR Part 563 1 established a clear definition of an EDR: Event Data Recorder (EDR) means a device or function in a vehicle that records the vehicle's dynamic time-series data during the time period just prior to a crash event (e.g., vehicle speed vs. time) or during a crash event (e.g., delta-V vs. time), intended for retrieval after the crash event. For the purposes of this definition, the event data do not include audio and video data.

What about data recorded by vehicles when they are not involved in a crash? Is that EDR data? Perhaps not, given the definition established in Part 563.

However, as outlined in the first edition of Crashology2, there is significant data recorded by vehicles on an ongoing basis or in response to events that would not be categorized as a crash. These data may be just as valuable as the EDR data collected from a crash, or, potentially, the only data available following a crash, depending upon circumstances, including severity.

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One source of such non-crash data is known as the Toyota Vehicle Control History (VCH).

Toyota has been one of the most forthcoming manufacturers with its EDR data in terms of historical coverage and the volume of data retrievable by investigators. In addition to this, Toyota recognized the importance of recording vehicle behavior and driver operations during events that may not result in a crash, or at least a crash of sufficient severity to cause the recording of an EDR event.

The VCH is a function that records control data (record data) when triggered by specific vehicle behavior.

The VCH also stores image data when a specific trigger is set. The VCH is recorded in different storage areas designated by trigger group and it is possible to save up to 140 items in total. If storage space is filled, the data is overwritten on a First-In, First-Out (FIFO) basis. Each trigger receives time information from the main body Electronic Control Unit (ECU), which is the time elapsed since the engine switch was turned on for the given ignition cycle. Absolute time may be recorded for vehicles with factory-installed navigation systems.

Data are recorded in the airbag ECU, and although it can be retrieved utilizing Toyota Techstream, it cannot be cleared using this application. Disconnecting power from the vehicle will also not result in VCH data loss. Any images stored as the result of specific triggers are stored in the camera sensor electrically erasable programmable read-only memory (EEPROM).

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The VCH data is obtained utilizing the Toyota Techstream software 4 and a suitable connection to the vehicle’s OBD-II port. Tools that have been successfully used for connection include the MongoosePro Toyota 2 cable 5, Nexiq USB-Link 6, and the Bosch CDR 900 7.

not associated with PCS or Intelligent Clearance Sonar (ICS) can be seen in Figure 2.

One of the more advanced and potentially valuable data elements available from VCH are images taken by the system as a result of specific triggers. These triggers include: •

PCS operation history - pre-collision brake assist operation (PCS brake assist operates)

•

PCS operation history - prior brake operation (PCS preliminary braking operates)

•

PCS operation history - pre-collision brake operation (PCS brake operates)

The recording details of data that is associated with PCS/ICS can be seen in Figure 3.

Figure 1: Splash screen for the Toyota Techstream The ability to recover VCH data began with the 2013 RAV4 and 2014 Highlander. This feature has subsequently been added to nearly all Toyota and Lexus models. The VCH data available from supported Toyota vehicles can vary by model. Table 1 shows a partial list of the triggers that generate VCH recordings in a 2018-2019 Toyota Camry. Reviewing this abbreviated list shows that many triggers are associated with accelerator pedal opening angle, gear shift selector changes, the Pre-Collision System (PCS), Vehicle Stability Control (VSC), Traction Control (TRC), Anti-lock Braking System (ABS), Lane Keeping Assist (LKA), and Lane Departure Alert (LDA) systems.

As evident in Table 1, many of the trigger thresholds associated with VCH could be met during aggressive driving or in the course of an avoidance maneuver that did not ultimately result in a crash. Because of this, VCH data is potential evidence not only for collision investigation, but also the investigation of any incident that involves a supported Toyota vehicle, including racing incidents, police pursuits, fleeing suspects, etc.

Table 1 lists the number of data samples and duration of the recording associated with the pre- and posttrigger VCH recordings. The recording details of data

In July 2019, the authors conducted controlled testing of a 2019 Toyota Camry. The vehicle was instrumented to assess the accuracy of many recorded values in its VCH. Detailed results of that testing are being compiled for a future technical article in Collision Magazine.

Figure 4 shows a sample of the VCH data collected from a single trigger - in this case, the trigger was “Accelerator pedal opening angle signal is high immediately after brake pedal is released.”

Figure 5 shows a sample of the VCH data collected from a separate trigger - in this case, the trigger was, “Sudden turning history.”

Figure 6 shows a VCH record generated by the trigger, “Accelerator pedal opening angle is medium or higher immediately after shifting to R.” Figures 4, 5 and 6 represent a small portion of the data collected from the Camry testing, but are sufficient to show that the data are detailed and extensive.

VCH data are a tremendous source of potential evidence in many types of vehicular investigations. The engineering of this technology into vehicles and the availability of publicly-available tools to acquire these data are exclusive to Toyota at this time. Investigators should certainly be aware of this data source and always consider gathering these data in any case involving a supported Toyota vehicle.

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2018-2019 Model Year Toyota Camry

ID No.

Record Data Before Detection After Detection Recording Number Recording Number Sample Time (sec.) of Points Time (sec.) of Points Frequency (Hz)

Trigger

Accelerator pedal opening angle signal 4 is high immediately after brake pedal is released Accelerator pedal opening angle is 5-1 medium or higher immediately after shifting to R Accelerator pedal opening angle is 5-2 medium or higher immediately after shifting to forward position Accelerator pedal opening angle signal 5-3 is high immediately after brake pedal is released Accelerator signal and brake signal 8 input simultaneously Medium or higher accelerator signal 9 input immediately after switching to D or R Medium or higher accelerator signal 10 input during N 12 VSC operation history 13 TRC operation history 14 ABS operation history 15 Sudden braking history 16 Sudden turning history Accelerator pedal opening angle signal 17 is high during cruise control operation PCS operation history (warning 18-1 buzzer operation) PCS operation history (warning brake 18-2 operation) PCS operation history (pre-collision 18-3 brake assist operation) Pedestrian protection system over 18-14 constant pressure sensor signal input PCS operation history (side airbag 18-15 operation request) Torque sensor signal above certain 19 amount is detected during LKA/LDA operation

7.5

15

7.5

16

2

5

10

5

11

2

5

10

5

11

2

5

10

5

11

2

5

10

5

11

2

5 5 5 5 5

34 34 34 34 34

5 5 5 5 5

35 35 35 35 35

6.7 6.7 6.7 6.7 6.7

5

10

5

11

6.7

5

10

4

9

2

8

20

0

1

2.5

Table 1: Partial list of VCH triggers for 2018-2019 Toyota Camry

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Figure 2: Non-PCS/ICS data recording

Figure 3: PCS/ICS data recording

Figure 4: VCH data from “Accelerator pedal opening angle signal is high immediately after brake pedal is released” www.collisionpublishing.com

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Figure 5: VCH data from “Sudden turning history”

Figure 6: VCH data from “Accelerator pedal opening angle is medium or higher immediately after shifting to R” (EDR) Developed by Toyota Motor Corporation, SAE International, Technical Paper 2016-01-1495

References 1. https://www.govinfo.gov/content/pkg/CFR-2011-title49-vol6/pdf/CFR-2011-title49-vol6-part563.pdf 2. Wesley Vandiver and Robert Anderson; Vehicle System Forensics for Crash Reconstruction, Crashology: The Science of Crashes, Collision – The International Compendium for Crash Research, Volume 13, Issue 1

4. https://techinfo.toyota.com 5. http://www.drewtech.com/mongoose/toyota2.html 6. https://www.nexiq.com/Product/Detail/125032 7.

https://www.crashdatagroup.com/cdr900/

3. Motomi Iyoda, Tom Trisdale, Rini Sherony, Daniel Mikat, and William Rose, Event Data Recorder 46

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Toyota Gen1 EDR Event Recording Logic W. R. Rusty Haight, Collision Safety Institute Robert Anderson, Biomechanics Analysis

I

ntroduction This paper addresses the analysis of more than 100 CDR/CDRx files retrieved from crash test vehicles and a total of more than 600 Toyota system CDRx files.

This was done to examine and better understand what is identified as the “Gen1" airbag control module’s process of overwriting previously recorded events and offers a more detailed examination of what is meant by the term “prior event” found as an event chronology identifier in some Gen1 system Event Data Recorder (EDR) components.

The recording trigger threshold of hose systems and the recording order and other characteristics of the Gen2, 3 and 4 system is different than what is observed in Gen1 systems and is not addressed here.

is a Toyota followed by, for example, a Ford. Ultimately, both the stopped car, the Toyota and Ford are involved in a crash. The question comes down to: who hit whom first?

Did the Toyota run into the stopped car and then hit by the Ford or, while slowing, did the Ford hit the Toyota and push it into the stopped car? During the investigation, data is retrieved from the Toyota and the events recovered are as seen in Figure 2.

But why?

There are almost any number of scenarios and data sets one might have already seen or might be able to envision where understanding the recording logic for Toyota ACMs would be useful in better understanding and evaluating a given collision sequence. Not to exclude the same sort of analysis for crash data which might be recorded in another manufacturer’s ACM/EDR component. Toyota EDR data - particularly as it relates to “Gen1" system - is fairly unique and it’s clear that a better understanding of that system’s recording logic deserves a closer look.

Consider an example of data retrieved from a Toyota Airbag Control Module (ACM) such that the “Front/Rear Event Record Summary at Retrieval” data table, as seen in Figure 1, reports that there were three events recovered and, chronologically, they are numbered Triggers (TRGs) 5, 13 and 14. On the surface, what can be said about the chronological order and what happened to, for example, TRGs 6 through 12 in Figure 1?

It’s not hard to imagine a scenario involving, for example, multiple vehicles and impacts some of which rise to the level of recorded or recordable events creating a string of records which, progressively in chronological order, are recorded and then overwrite previously recorded events such that, when the sequence ends, leaves event records as seen in Figure 1.

Imagine a crash scenario described such that a car is stopped at a traffic signal and, approaching that car from behind

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In Figure 2, there are TRGs 4, 6, and 7. What kind of crash was TRG 5? Why does it appear that TRG 5 was overwritten by a later event and TRG 4 wasn’t? Would knowing more about what TRG 5 was (in terms of perhaps polarity or severity) be helpful in establishing the order of events?

Background As detailed in the Bosch Crash Data Retrieval (CDR) Tool help file and the Data Limitations 1 text associated with a CDR Tool data translation report for one of the Toyota line vehicles,2 there are, at this writing, four generations of Toyota Airbag Control Modules (ACMs) which include the Event Data Recorder (EDR) functionality which is accessible using the Bosch CDR Tool. Generation 1, or “Gen1,” refers to what are identified in the report as

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Electronic Control Unit (ECU) “00EDR” and “02EDR” systems. Gen2 refers to “04EDR” and “06EDR” systems, Gen3 refers to, for North American sales, the “12EDR,” “13EDR,” and “15EDR” systems 3 and Gen4 which, thus far, includes “17EDR” and “19EDR” systems. The number in the generation designator (i.e.: the 02 in the 02EDR designation of a Gen1 system) is most closely associated with the first year the particular ACM/EDR variant was introduced in production.

data sets from various Toyota line vehicles from production year 2000 through 2019. Many of these tests were conducted to evaluate or verify polarity, range, resolution, rounding, timing and accuracy of the EDR reported data versus that of reference instrumentation and comparison with known circumstances of the crash (i.e.: seat position or driver’s seat belt status). ACM behavior such as trigger threshold,4 accelerometer bias, and recording threshold related to overwriting or the replacement of previously recorded events has also been investigated and will be addressed separately.

Toyota line systems with Gen1 ACM’s span the 2001 to 2011 production year range for North American distribution and may be found in vehicles made for sale outside North America through 2013. As such, Gen1 ACMs are still a fairly frequently encountered system and their event recording behavior warrants analysis and discussion.

Gen1 ACMs (ECUs) typically store, at any one time, up ECUs in other generation groups have few recording char- to three longitudinal events, the deployment algorithm5 acteristics which overlap those found in Gen1 systems and trigger and recording thresholds are generally related their recording logic, in particular, does not mirror that meaning that once the module experiences sufficient lonfound in Gen1 systems as would be discussed here. For gitudinal acceleration (either positive or negative along the example, where Gen1 systems may record, at any one time, longitudinal axis) to engage the ACM’s decision making up to 3 longitudinal events, those in other systems only algorithm (a trigger threshold) that will result in a set of have space for two recorded longitudinal events at any one event data being recorded as an event record (assuming sufficient residual power to complete the recording and time. that the module’s EDR function hasn’t already been “froTo date, the authors have conducted over 100 instrument- zen.” A small number of these vehicles/modules may ined crash tests involving Toyota vehicles with Gen1, 2 and clude pre-crash data and most report longitudinal delta-V 3 ACMs and have undertaken a review of some 600 EDR for a period of 150 ms or more. www.collisionpublishing.com

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Figure 1: Toyota Airbag ControlModule (ACM) “Front/Rear Event Record Summary at Retrieval” data table (above) Figure 2: Event Record summary from a Gen 1 Toyota module (left)

Figure 3: “System Status at Time of Retrieval” data table Module Generation Identification In Toyota line CDR Tool translation reports, normally in the “System Status at Time of Retrieval” data table, one will find a reference to “ECU Generation” (Figure 3). One might easily assume that the reference to “ECU Generation” (i.e.: “02EDR”) corresponds with or in some way refers to the production or model year of a given vehicle; however, that assumption would be incorrect.

For a given vehicle line, the data recording specifications associated with a generation identifier generally span more than one ECU category (ECU generation). For example, the Camry model is supported for data retrieval using the CDR Tool for production years 2001-2011 (the discussion here limited to Gen1 systems). In that CDR Tool coverage period, one might find a variety of ECU generation identifiers which do not necessarily correspond to the production year for that vehicle including, some which fall into the Gen2 group (beyond the scope of the discussion here).

Based on a review of over 600 individual CDR Tool files, the authors have identified, for example, that the 00 and 02EDR generation ECU might be found in the Camry in production year 2001-2005 models. By comparison, the 04EDR ECU (Gen2) might be found in a Camry from production years 2005-2011.6 The CDR Tool help file listing for Toyota line vehicles, in general, cross-references the vehicle model to an ECU generation and a direct-to-module CDR Tool cable called out for that vehicle by model and/or by ACM part number. Using the Camry as an example, one might compare 50

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the ECU Generation year range, CDR Tool cable number and at least some of the identified ACM part numbers for ACMs and find a range of ECU generations applicable to the Camry during those production years. The same analysis might be done for a variety of Toyota systems with the ultimate result being a comparison of characteristics and/ or behaviors using one or more of the listed cross-referenced criteria. In the final analysis, to understand module behavior, we should be interested in the characteristics of an ACM type not a vehicle model named as a sales identity meaning we should be evaluating generations and module types rather than the vehicle model.

In the 02EDR module generation group, data from the Camry can be retrieved using either the F00K108615 or F00K108617 cable (ultimately depending on part number). The Camry shares the F00K108615 cable with other Toyota models including the Tundra, Avalon, Tacoma, Corolla and Matrix (and, by extension, the Pontiac Vibe).

The behaviors identified as common to the Camry would then also be expected in these other model vehicles; all of which may have an 02EDR module and are accessible using that direct-to-module cable. By extension, for this study, the analysis of several systems identified chiefly by model name here for convenience should not be considered to exclude similar module type systems but rather, be more inclusive of those systems regardless of the name plate. In other words, if a Camry and a Corolla from the same ECU generation would be identified as having similar data characteristics (a) because they share a generation category identifier and (b) would be accessible using the

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same direct-to-module cable number. In short: the “name plate” on the car doesn’t, by itself, “mean anything” when it comes to evaluating the data potential and behaviors of a module type.

Trigger Threshold For a discussion to move forward here, there must be a frame of reference for certain terms. Within the lexicon of EDR/CDR data the terms “trigger,” “trigger threshold,” “recording trigger” and “recording threshold” are used at various times and may or may not have similar meaning or application. To narrow this discussion, and for clarity, we may first dismiss the Toyota line Gen3 and Gen 4 systems from this discussion because, of the four Toyota ECU generations, the Gen3 and 4 systems are calibrated to functionally separate the often mixed terms associated with the phrase “trigger threshold” where one might mean “to start the deployment algorithm” and the other might mean “to start the recording of crash related data.”

However, in all cases, before any ACM may record data, it has to first detect or “recognize” there has been a suitable physical occurrence; generally a planar impact of some sort or a dynamic event suggesting the potential for a rollover.

Reaching or exceeding this initial “detection threshold” is what would cause the deployment decision making algorithm to “activate” or to begin running.

For Toyota Gen1 systems, it has generally been accepted that if ACM begins its occupant restraint control algorithm, it is designed to attempt to record data associated with that algorithm having run and, for that reason, the two are believed to have the same threshold level. In all cases, the recording potential requires that sufficient internal power is available for digital recording after the event and that there is available recording space (meaning that recording is not prohibited as a function of a previously recorded deployment event and the “Freeze Signal” set8,9).

Beyond that, we find, in the Toyota line systems Data Limitations text an entry on point here: “... For the time indicated in "Lateral Delta-V", "Roll Angle" or "Lateral Acceleration", the first sampling point after the recording trigger establishment is regarded as time zero. The time zero of the data and the recording trigger establishment do not always occur simultaneously. … The recording trigger judgment threshold value differs depending on the collision type (i.e., frontal crash, rear crash, side crash, or rollover event). …” 7 (emphasis added) Notably, here, the initial passage references lateral (side impact) data and for Gen1 systems which are the subject of

this discussion, lateral crashes and rollovers are excluded.

There is no similar reference found in the Data Limitations8 text for the longitudinal component; however, we find this generally associated passage: “… Longitudinal Delta-V" indicates the change in forward speed after establishment of the recording trigger. This does not refer to vehicle speed, and it does not include the change in speed during the period from the start of the actual collision to establishment of the recording trigger. …” 9 (emphasis added) The highlighted portion of this passage lends itself directly to the previously understood concept of what threshold was required to activate the occupant restraint control algorithm in a Toyota line system for a “Front/Rear” 8 crash event. By extension, the idea that when the longitudinal “algorithm activation threshold” has been met, the “recording threshold” is also met and, assuming power and recording space availability, data would be recorded (for “Front/Rear” event/events)9.

As it relates to planar impacts and, here, specifically to “Front/Rear” events, ACMs have a calibrated level of acceleration required for the ACM to “wake up” or activate the occupant restraint control algorithm. In this context, it might be said that, for a longitudinal event in a Gen1 (and Gen2) Toyota line system, the “algorithm activation” or “trigger threshold” has been identified as 2g.

The crash characteristics required to reach that level and a discussion of testing done to establish that specific level is the subject of a follow up article in Collision Magazine. However, this trigger threshold can be investigated by evaluating data from a reference accelerometer output and ACM downloads for whether or not an event was set and comparison to that reference dataset. For most auto manufacturers supported for data retrieval using the CDR Tool, it has been generally assumed that the longitudinal trigger threshold is greater than 1 but less than or equal to 2 g’s calibrated to a vehicle’s design, stiffness characteristics and general intended use.

Toyota Gen1 modules record a “Trigger (TRG) Event” whenever the modules’ decision-making algorithm is activated. The TRG events are recorded into memory pages in the ACM sequentially and assigned an incremented TRG number. As part of this test series, the logic of the sequence realted to where data is recorded to those memory pages is examined for those ACMs which the capability to store 3 events. Of particular interest is the rationale for over-writing some events while other events persist with successive triggers.

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GEN1 System Details

Recording Logic Overview

Toyota Gen 1 ACM data sets are identified as being related to 3 suppliers or manufacturers: Denso, TRW and TMC (Toyota Motor Company). The supplier for any given module can be identified by examining the Data Limitations identifier code at the end of the Data Limitations section of the CDR data translation report. For example, “05004_ToyotaDENSO_rnnn,” “05002_ToyotaTRW_ rnnn,” and “05002_ToyotaTMC_rnnn" are examples of the designations for Gen1 Denso, TRW and TMC systems, respectively.

As a function of the review of the multiple crash test series conducted (in addition to a review of field data), the recording logic for Gen1 modules becomes clearer. There are some initial conditions for Gen1 systems which may be module supplier dependent (as seen below) but the recording logic otherwise follows a relatively clear pattern.

The common characteristic across these three Gen1 9 system types is that they all may, at any one time, have multiple recorded “Front/Rear” events. For the TMC system, these events will be recorded to memory pages 0 and 1 or pages 0, 1 and 2 inasmuch as, for some variants of this system, there may only be two available memory pages.

For the Denso and TRW systems, there are three memory pages, identified as Pages 0, 1 and 2.

Up to the specified limit, Gen1 module data sets include the designation “Most Recent Event,” “First Prior Event” and “Prior Event” for those events (as seen in Figure 1).

The designation “Prior Event,” does not necessarily indicate the event or crash record which would immediately precede the “First Prior Event:” “... "Prior Event" is the event that occurred before the "1st Prior Event" that reached the greatest MAX Delta-V. Therefore, "Prior Event" is not always the prior event of "1st Prior Event". ...” 9 In this passage, the “Prior Event” is the event that occurred before the “1st Prior Event” that reached the greatest (MAX) delta-V. The statement suggests that the “Prior Event” is not always the immediate previous event relative to the “1st Prior Event” indicating that a recording priority is only with preserving previously recorded events with greater magnitude delta-V. While this passage would seem to indicate that when a new TRG occurs, it may be expected that the module will decide to over write the “1st Prior Event” or “Prior Event” memory page based upon delta-V magnitude alone, in this analysis, the authors identified that this isn’t always the case. For delta-V’s of the same magnitude, negative versus positive delta-V’s and relative age are also considered. In fact, polarity stands out as a primary consideration, even before the delta-V’s magnitude. Based on testing, the authors have developed and offer a better understanding of the recording logic for the Gen1 module family.

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One aspect of the process - and one which applies beyond Toyota Gen1 systems to others in use by Toyota to include later generation systems - is that at any point in time, the recorded event identified as the “Most Recent Event” or the most recent recorded event is always “protected” (digitally flagged) to prevent it from being overwritten by the immediately subsequent event. In this way, every data set retrieved in which multiple events (whether or not related to each other) had been recorded to the module would include, at a minimum the “Most Recent Event” and the immediately “First Prior Event.”

For Gen1 TMC systems and later systems with only two “Front/Rear” memory pages (for example a Gen2 or even Gen3 system and to the exclusion here of a discussion of the potential for “Side” or “Rollover” event recording), the process becomes relatively straightforward. Recording begins at Page 0, then the next event is recorded to Page 1, then the next event is recorded over what was at Page 0, and so on until the “Freeze Signal” is set. For Gen1 systems with 3 memory pages, where there are three available memory “Pages” for data recording they are identified as Page 0, 1 and 2, the process is laid out as follows: 1. When the ACM detects “its” first event, that event will be recorded to “Page 0" and identified as the “Most Recent Event,” identified as TRG 1 and is “protected” 2. When the ACM detects a second event, the event in “Page 0" which was previously “protected” will not be overwritten but its designation is changed to “First Prior Event” (of course, it remains TRG 1) a. The new second event is recorded to Page 1 and identified as the “Most Recent Event”and identified as TRG 2 3. When the ACM detects a new (3rd event), single the event previously recorded to “Page 1" is “protected,” the new event will be recorded to either “Page 0" or “Page 2" depending on... a. If the event in “Page 0" has a positive longitudinal delta-V, then it is overwritten by the new “Most Recent Event” and identified as TRG 3

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b. If the data in “Page 0" has a negative longitudinal delta-V, it is preserved and the new “Most Recent Event” is recorded to “Page 2" and identified as TRG 3 (in this way, we see that a negative longitudinal deltaV event is prioritized when recording takes place) 4. When the ACM detects a new (4th event) the process continued as seen in the flow chart seen here as Figure 4.

To illustrate the application, a series of crash tests were conducted using a Toyota Sequoia with a Gen1 Denso ACM. The first detected event was created to be a positive delta-V event and was recorded to “Page 0" as seen in Figure 5. The vehicle (module) was then exposed to a series of tests which illustrate the recording logic process “in application” as shown in Figures 6 and 7. In comparison, as a product of a series of tests conducted with Toyota Camry with a Denso ACM, where the first event resulted in a negative longitudinal delta-V, the process of protecting and recording events following the flow chart (Figure 4) can, again, be clearly identified as seen in Figure 8.

Limitations Until the module attempts to record an event and lacks sufficient power to complete the recording, or a “Freeze Signal”8 has been set, this process will continue until a maximum number of TRGs has been set seen as:

"... "TRG Count" indicates the number of frontal/ rear recording triggers that have been established.

The calculated value does not include the number of times side or rollover recording triggers have been established. The sequence in which each frontal/ rear event occurred can be verified from the "TRG Count". The lesser the "TRG Count" value, the older the data. The upper limit for the recorded value is 254 times. When more than one event reaches the upper limit, the actual "TRG Count" may be greater than what is displayed for that event. ..."9 The question is: what happens after TRG 254 and does examining that potentiality fit the model described here (and perhaps lead to or better explain other observations)?

For that, one might look to the Case Problem offered in this issue of Collision.

Conclusions Based on the analysis of more than 100 CDR/CDRx files retrieved from crash test vehicles and a total of more than 600 Toyota system CDRx files, this paper offers a better understand what is identified as the "Gen1" airbag control module's process of overwriting previously recorded events and provides a more detailed examination of what is meant by the term "prior event" found as an event chronology identifier in some Gen1 system Event Data Recorder (EDR) components.

Figure 4: Toyota GEN1 recording logic www.collisionpublishing.com

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Figures 5 (top), 6 (middle) and 7 (bottom): Toyota Gen1 recording logic process 54

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Figures 8: Toyota Gen1 recording logic process The flow chart (Figure 4) showing the module type’s recording logic can be used in both directions. Meaning one may follow the logic and predict how the next occurring event would be recorded as well as going “backwards,” one may use the logic flow chart to establish what event(s) would have been overwritten to get to the currently recorded set of data.

Looking back to the example data set seen in Figure 2 and the question: what was TRG 5 or why was TRG 5 overwritten? In the context of a set of crash information (the human, vehicle and environmental information available after an investigation) this process may lead one to understand what TRG 5 was (in terms of polarity and its relevance to TRG 6 and possibly later events).

6.

Footnotes 1.

2.

3.

4.

5.

From the “General information” section the Data Limitations text found in a Toyota line vehicle: “…The data recording specifications of Toyota's airbag ECUs are divided into the following categories. The specifications for 12EDR or later are designed to be compatible with NHTSA's 49CFR Part 563 rule. - 00EDR / 02EDR / 04EDR / 06EDR / 10EDR / 12EDR / 13EDR / 15EDR / 17EDR / 19EDR …” (example from 05017_ToyotaS00std_r026 CDR Tool software ver 19.1) “Toyota line” or “Toyota” in this paper is offered to include, by reference, similar Toyota, Lexus and Scion line vehicles supported for data retrieval and translated using the current version of the Bosch CDR Tool.

There is a reference to 10EDR, which is not an ACM type normally found in Toyota line vehicles sold in the North American market.

While a definition of “trigger threshold” (“… Trigger threshold means a change in vehicle velocity, in the longitudinal direction, that equals

7.

8.

9.

or exceeds 8 km/h within a 150 ms interval. For vehicles that record “delta-V, lateral,” trigger threshold means a change in vehicle velocity in either the longitudinal or lateral direction that equals or exceeds 8 km/h within a 150 ms interval. …”) is found at 49CFR563.5. That regulation applies to vehicles made for sale on or after September 1, 2012. Gen1 systems installation stopped before that date and therefore that definition does not apply to Gen1 Toyota systems.

As found in at least one variant of the Toyota line Data Limitations text associated with a CDR Tool translation report, the trigger threshold is described as “… (T)he point in time at which the recording trigger is established is regarded as time zero for the recorded data.

For the time indicated in "Lateral Delta-V", "Roll Angle" or "Lateral Acceleration", the first sampling point after the recording trigger establishment is regarded as time zero. The time zero of the data and the recording trigger establishment do not always occur simultaneously. …”

The CDR Tool Help file also lists cable number F00K108616 as potentially one which may be required for direct-to-module access for a Camry, presumably an 06EDR type given other vehicles which have actually called for that particular cable by part number; however, the authors have not identified any North American sales Camrys actually requiring that cable by ECU part number(s) in the data reviewed.

As found in the Data Limitations text 05002_ToyotaTRW_r027 using CDR Tool software version 19.1 As used for Gen1 Toyota systems, the designation “Front/Rear” means “to the front” OR “to the rear” of the vehicle. “Front/Rear” is sometimes misinterpreted to specify a “front-to-rear impact” and while that is one part of the intended designation, the designation also includes a “rear-to-front” impact.

Gen2 systems, those closest in comparison to Gen1 systems, have two memory pages for data recording and are excluded from this analysis.

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Toyota Gen1 EDR Event Recording Logic

Case Problem W. R. Rusty Haight, Collision Safety Institute Robert Anderson, Biomechanics Analysis

T

his case problem was designed to tie into the article in this issue of Collision regarding the “Toyota Gen1 EDR Event Recording Logic” and reinforces the need for a careful reading of all the contents of CDR Tool data translation reports to fully grasp their meaning and value.

is, after all, from a series of crash tests designed to address the issues raised in the case problem.

In this case, you’re working on a crash involving a 2006 Toyota Corolla. You use your Bosch CDR Tool to access and retrieve crash data from the Toyota. Using the latest version of the CDR Tool software, you generate a translation report, select data tables of which are displayed here in this issue of Collision. In fairness to the reader, it should be made clear that this data comes from a series of crash tests conducted specifically to investigate Toyota ACM trigger/ event recording sequence, limitations and processes.

2. Although all three events are identified as “Most Recent Event,” determine, is you can, the actual chronological order of the recorded events?

As you can see from the report pages, there are three retrieved events, all are marked “Most Recent Event.” All are identified as TRG 254 (Trigger number 254).

For the purposes of this case problem, assume the crash you are investigating is a rearender; you have every reason to believe that this Toyota was struck from behind at a relatively low speed by another vehicle. Although the normal process would be to first try to evaluate whether or not the retrieved data is related to the crash you are investigating, for this case problem you may rely on the information that one of the recorded events is actually from the described low speed rearender crash.

Of course, in practical application, you’d want to be able to find a set of facts or conditions which could be used to affirmatively say that some or all of the data you have to work with IS related to the crash you’re investigating.

Here, this is a case problem and you are given certain assumptions: (1) you may adopt the information as reliable and (2) one of the recorded events is actually from the described low speed rearender crash. The data presented here 56

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That said, the questions for this case problem are: 1. Explain why the TRG (trigger) count numbers are all identified as TRG 254, Most Recent Event.

For the next part of the case problem, assume that, after the minor crash which lead to the recorded data as shown, the driver of this car was involved in another crash. In that subsequent crash, this driver was again rearended by another car. You may adopt as fact that the longitudinal delta-V in the subsequent crash was about +6.1mph.

3. Can you say whether or not there would there be any new data (a new recorded event) as a function of this subsequent rear end crash?

4. If there was new data recorded as a function of the subsequent crash, which previously recorded “Most Recent Event” would be overwritten by data from this subsequent event?

5. If there was new data recorded as a function of the subsequent crash, what would the TRG number of the new event record be?

[Editor’s note: In the interest of page space, the data tables from the report have been formatted to fit the space available and the hexadecimal data has been omitted; it may be considered irrelevant to the focus of the case problem. One may wonder if any data or key element has been altered or otherwise excluded. The answer is no. You may rely on the data tables, as displayed, as having been translated from the original raw data using the latest version of the CDR Tool software and

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nothing has been edited, none of the translated values or report form have been changed apart from the previously mentioned reformatting. The reformatting essentially reduces three pages of translated data for each of three recorded events to one page for each of the three recorded/retrieved events. The events, as

named, are presented in the same order they would be presented in a “normally” formatted report and the original page numbers indicating where the data for each of the translated events began are indicated.]

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See page 128 for the solution to the case problem 62

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Management and Leadership Highway Safety Crime Scene and Forensic Science Police Motorcycle Community-Police Partnerships

Every Dimension of Public Safety Only at Northwestern.

More than 50 courses providing professional skills with a leadership perspective. On campus, around the country and online.

Find the right course for your department. nucps@northwestern.edu or 800-323-4011

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Motorcycle Accident Reconstruction: Incorporating EDR Data from the Struck Vehicle Nathan Rose

Neal Carter

Martin Randolph

ntroduction Electronic event data has become ubiquitous on modern passenger vehicles, and much of this data is accessible with either the Bosch Crash Data Retrieval (CDR) system or the Global Information Technology (GIT) system [Haight, 2013a]. This data will often contribute useful information to an accident reconstruction. However, reconstructionists will need to understand how the data is collected and its limitations. Issues of interpretation and accuracy arise that will often require the reconstructionist to employ classic reconstruction methods alongside the data from the event data recorder (EDR). Also, many accident reconstructions will seek answers to questions that go beyond what the EDR data can directly provide, and many accident reconstructions will need to draw on a broad swath of evidence – physical, video, audio, and testimonial, in addition to the electronic data. Many accident reconstructions will also draw on empirical data and principles of physics.

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William Bortles

Some motorcycles now have EDRs and the possibility of there being electronic data from the motorcycle involved in a collision now exists [Fatzinger, 2017 and 2018].

This scenario remains rare, however, and the more common scenario is that the struck passenger vehicle will be equipped with an EDR. This EDR data will give useful information but will not directly report the collision speed of the motorcycle. The electronic data can, however, be combined with classic reconstruction techniques (conservation of momentum, analysis of the post-impact translation and rotation of the struck vehicle, and damage analysis methods) to infer the collision speed of the motorcycle, to reconstruct the speed and path of the passenger vehicle in the moments leading up to the collision, and to understand errors that the driver or rider made in the moments leading up to the collision.

Newton’s 2nd and 3rd laws together (conservation of momentum) dictate that, during a collision between two vehicles, the ratio of the mass or weight of Vehicle #1 (m1

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or W1) to the mass or weight of Vehicle #2 (m2 or W2) is equal to the ratio of the change in velocity experienced by Vehicle #2 (ΔV2) to the change in velocity experienced by Vehicle #1 (ΔV1), as follows: (1) If the EDR data from the struck vehicle reports that vehicle’s ΔV, then this ΔV can theoretically be used to calculate the change in velocity of the motorcycle. The ΔV of the motorcycle can then be used to infer the impact speed of the motorcycle. However, potential problems can arise with this approach due to the large weight discrepancy that may be present between the motorcycle and the struck vehicle [Niederer, 1990; McNally and Bartlett, 2002; Strohmeyer, 2018]. As an example, the ratio of the weight of the struck vehicle (including the driver) to the weight of the motorcycle (including the rider) in one of the case studies examined later in this article was approximately 7.15.

The EDR-reported resultant ΔV in this instance was 12.1 mph. Applying Equation (1) yields a ΔV for the motorcycle of 85.8 mph, as follows (equation 2):

However, there is some level of error that could be present in the EDR-reported ΔV. Given the weight ratio, every 1 mph of potential error in the struck vehicle ΔV would produce 7.15 mph of uncertainty in the calculated ΔV for the motorcycle. If the potential error within the ΔV was ±3 mph, then the potential range on the ΔV for the motorcycle would be 65.1 to 108.0 mph, a 42.9 mph spread. On

the other hand, if the potential error within the ΔV were ±1 mph, then the potential range on the ΔV for the motorcycle would be 78.6 to 92.9 mph, a 14.3 mph spread.

This illustrates the sensitivity in conservation of momentum analysis due to the typically large weight ratio present in motorcycle-to-passenger vehicle collisions and emphasizes the need to understand what the potential error in an EDR-reported ΔV is likely to be in any given case.

The Accuracy and Usefulness of the Struck Vehicle EDR Data Pre-Crash Data A common motorcycle crash scenario occurs when an EDR-equipped passenger vehicle turns left across the path of a motorcycle and is struck by the motorcycle. In these instances, pre-crash EDR data can be helpful for establishing the specific characteristics of the attempted left turn that preceded the collision. This data may include speed, throttle percentage, brake applications, and steering angles for the struck vehicle. Bortles, Biever, Carter, and Smith presented a literature review related to the accuracy of the pre-crash speeds reported by original equipment event data recorders installed in passenger vehicles [2016]. These authors reported that “EDR reported vehicle [pre-crash] speed is typically measured by sensors monitoring the output of the transmission or an average of the speed of the drive wheels. These sensors can accurately report wheel speed but, due to certain factors, the wheel speed may not represent the true over-the-ground speed of the vehicle.

These factors may include longitudinal wheel slip due to acceleration or braking, wheel slip due to rotation of the vehicle about the vertical axis, significant changes in the tire’s rolling radius as compared to the vehicle’s original

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equipment, and changes to final drive ratio compared to the vehicle’s original equipment.”

Bortles et al presented Figure 1, which summarizes the error in the EDR-reported pre-crash speed for tests they culled from the various studies they reviewed. This graph parses this data by operational condition: steady state driving or heavy braking (with antilock brakes (ABS), without ABS, or with it unknown if ABS was present). None of the data points in Figure 1 are from tests where the vehicle developed a significant sideslip angle. This graph shows that under heavy braking, the reported pre-crash speed will typically be lower than the actual speed but within 4 mph of the actual speed. For steady driving, the EDRreported pre-crash speed will typically be lower than the actual speed but within 1 or 2 mph of the actual speed.

For a typical collision between a motorcycle and a passenger vehicle that occurs while the passenger vehicle is in the midst of a left turn, the driver of the passenger vehicle will typically not be braking heavily. For a typical left turn, the longitudinal acceleration of the passenger vehicle is likely

to fall in the range of -0.3 to 0.3 g. These maneuvers are closer to steady state driving than to the heavy braking scenarios included in Figure 1. It seems reasonable when reconstructing these collisions to assume that the EDR-reported pre-crash speed is within 2 mph of the actual speed, at least for the instant at which the speed is measured.

One challenge to using pre-crash speed data to reconstruct a collision between a motorcycle and a passenger vehicle is to determine exactly how the data syncs to the crash. Suppose, for example, that an EDR reports 5 seconds of precrash data at 0.5-second intervals. How should one interpret the speed reported at time zero? This depends on the specific system, but for some systems this reported speed at time zero will simply be the last speed reading collected by the ACM prior to the collision, which could be anytime between 0 and 0.499 seconds prior to impact. It is likely that the best way to handle this situation is to consider both ends of this range, considering the degree to which the vehicle is accelerating or decelerating over this ½-second interval. This will be illustrated in the case studies later in this article. Some ACMs do sample the pre-crash data

Figure 1: Accuracy of EDR-Reported Pre-Crash Speed 70

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elements at the time of the triggering event, and for these ACMs, this consideration would not be necessary.

Another issue with pre-crash data is that the multiple data elements reported may be asynchronous. This asynchronicity arises from the fact that not every sensor can be using the CAN bus to transmit their message at the exact same time, so the CAN messages get staggered. The sensors take their measurements and send them, but a controller decides what messages get to use the CAN based on the priority assigned to that sensor and a message priority from within the sensor. As a result, the levels of asynchronicity is directly related to the volume of CAN traffic at any given time and the priority of those competing messages. It is difficult to know the exact level of asynchronicity between the speed and steering input readings, though simulation could help to illuminate this.

Finally, sensors that measure and report the pre-crash data elements can have full-scale values that are too limited to capture the full value of a data element. For example, some systems have a limit on the highest speed they can report and others a limit on the magnitude of the steering input they can report. These limitations are often listed in the data limitations section of the CDR report and they will need to be accounted for within the reconstruction.

Crash Data (ΔV) In addition to the pre-crash data, the struck vehicle ΔV reported by the EDR can be useful for determining the collision speed of the motorcycle. The configuration of the collision can be determined based on the intersection geometry and the vehicle damage. Then, the principle of conservation of momentum can be used to determine the motorcycle speed necessary to cause the reported struck vehicle ΔV. The specifics of the reported ΔV will vary from system-to-system. Some vehicles will report only the longitudinal ΔV experienced by the struck vehicle. Others will report both the longitudinal and lateral ΔVs. In a companion article to this one, we summarize much of the literature related to the accuracy of EDR-reported ΔVs [Rose, 2019b]. The findings from this companion study will be summarized here.

First, the following factors can affect the accuracy of the EDR-reported speed change (ΔV): 1. There can be a ΔV that occurs prior to the impact accelerations exceeding the event-triggering threshold (algorithm enable or AE) for the EDR. 2. The recording window for the ΔV may be too short to capture the full ΔV.

3. The recording window for the ΔV may be too long and a ΔV experienced by the vehicle due to post-impact tire and dragging forces could be recorded.

4. The peak accelerations during a collision can exceed the capabilities of the accelerometer in the airbag control module (ACM), in which case the system will not capture the peak accelerations.

5. The ACM may reside some distance from the center of gravity (CG) of the vehicle. This is relevant because accident reconstruction calculations often calculate and utilize the CG ΔV. In instances where the collision induces significant rotation, the ΔV of the struck vehicle may need to be adjusted to more accurately reflect the ΔV at the CG of the struck vehicle. Bundorf [1996], Marine and Werner [1998], Rose [2007], and Haight [2013] describe methods for making this adjustment.

6. Physical damage to or displacement of the ACM can affect the accuracy of the reported ΔV. 7. The EDR could lose power before the collision is complete and some of the data may not get recorded.

None of these are measurement errors, per se. The first is inherent to any event triggered system or algorithm. Error sources two through four are hardware limitations and their effects will often be detectable through analysis of the EDR report. A reconstructionist will sometimes be able to correct for them. The fifth error source is simply something that the reconstructionist will need to account for in cases where there would be a significant discrepancy between the ΔV experienced at the ACM and that experienced at the CG. This will typically be instances when there is significant post-impact rotation. The sixth and seventh error sources amount to physical damage occurring to the measuring device itself or the power source. Errors in the reported ΔV in instances where there is actual damage to the ACM are not surprising and the reported ΔV may not be useable in those instances.

Some of these error sources are unlikely in collisions between motorcycles and passenger vehicles. For example, because of the typically large weight ratio between the vehicles in most motorcycle versus passenger vehicle collisions, the ΔV experienced by the passenger vehicle will often be less than 10 mph. At this level of severity, the ACM is unlikely to experience physical damage or displacement (error source #6). Similarly, the accelerations experienced by the struck vehicle are unlikely to exceed the full-scale value of the accelerometer (error source #4).

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The companion article concluded that the studies related to the accuracy of EDR-reported ΔVs in aligned, lowseverity collisions are likely to be the most applicable to motorcycle-passenger vehicle collisions. These studies consistently show the EDR-reported longitudinal ΔVs underreporting the actual longitudinal ΔV, generally by a magnitude of 2 mph or less, and most of the time by a magnitude of 1.5 mph or less. This is confirmed by the results from Beck et al [2006], which were from motorcycle collisions with EDR-equipped vehicles. The results from Beck were within the same range of EDR-reported ΔV errors as the other low-severity studies. Based on these studies, a reasonable range on the actual longitudinal ΔV for the struck vehicle would have a low-end of the ΔV reported by the EDR and a high-end of 1.5 to 2 mph greater in magnitude than what is reported by the EDR. So, for example, if the EDR-reported longitudinal ΔV was -5 mph, the reconstructionist could apply a range for the actual ΔV between -5 and -6.5 mph. A reconstructionist could also use the EDR reported longitudinal ΔV as reported and this would result in a conservative (low) estimate of the motorcycle’s ΔV and impact speed. Based on the Carr et al study, a similar range could be applied to the EDR-reported lateral component of the ΔV, though perhaps the range should be centered on zero (±1 mph, for instance).

cant momentum to the vehicle and their mass would not be included with the motorcycle’s mass in the conservation of momentum calculation. In other collisions, the rider separates from the motorcycle and then fully engages the vehicle. In these instances, the full weight of the rider would be included in the momentum calculation. Finally, in some instances, the rider’s speed is reduced by interaction with the vehicle, but the interaction with the vehicle is insufficient to cause the rider to reach a common velocity with the vehicle. In these instances, a portion of the rider’s weight can be included in the impact analysis.

Analysis Using Conservation of Momentum

In 1970, Severy, Brink, and Blaisdell reported seven staged collisions involving motorcycles. The motorcycles were carrying rider dummies and they struck either the front door or fender of a stationary passenger car (1964 Plymouth Fury). Three different motorcycle models were tested (Honda CL-90, Honda CB-350, Honda CB-750) with impact speeds of 20, 30, and 40 mph. Because these tests included rider dummies, they enabled Severy et al to make observations about the timing of the riders impacting the cars. Table 1 summarizes the timing for each of these collisions. As this table demonstrates, the collision between the motorcycle and the car is essentially complete prior to

Along with the potential sensitivity issue that can arise when applying conservation of momentum to motorcycle-passenger vehicle collisions, there are a couple of additional issues that could come up in the application of this principle. First, the reconstructionist will need to determine to what extent the rider’s body participated in the collision. During some collisions, the rider separates from the motorcycle and is projected beyond the impact without any appreciable interaction with the struck vehicle. In these instances, the rider’s body does not transfer signifi-

Another issue that can arise with the rider relates to the recording window of the EDR. Depending on the specific ACM involved in a collision, the recording window will typically vary between 100 and 300 ms, although some can have recording windows even shorter than 100 ms.

One of the ACMs from case studies considered in this article had a recording window of 50 milliseconds. For some impact configurations, there will be a delay from time that the motorcycle first collides with a vehicle until the time the rider collides with the vehicle. There is a possibility that a recording windows could be of insufficient duration to capture the collision between the rider and the struck vehicle.

127 128 129

30 30 20

First Contact (ms) 0 0 0

130

30

0

UNK

70

195

131 132 133

30 30 40

0 0 0

80 55 63

90 120 55

160 125 80

Experiment Motorcycle Impact Number Speed (mph)

Motorcycle First Contact of Forward Movement Rider with Vehicle Stopped (ms) (ms) 55 75 55 75 53 185

Rider’s Body Impacts Vehicle (ms) 130 120 195

Table 1: Timeline for the Tests Reported by Severy, Brink, and Blaisdell 72

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the occurrence of the collision between the rider and the car. The impact between the rider’s body and the vehicle in these tests did not occur until between 80 and 195 milliseconds after first contact between the motorcycle and the car. Thus, the impact between the rider and the car could occur outside of the recording window of the EDR. If this occurred, this would be another potential source of underreporting in the EDR-reported ΔV for the struck vehicle.

pillars and bumpers, doors, and fenders. These equations are as follows:

Of course, the probability of this occurring should not be over-emphasized. There is a speed dependence to the timing of the rider’s collision with the vehicle. When the rider separates from the motorcycle and travels forward into the struck car, they will often do so at a speed that is close to their initial impact speed. In other words, their interaction with the motorcycle will often not slow them appreciably.

The faster the rider is traveling, the more quickly they will cover the distance between the front of their motorcycle (which will often be the first component to strike the car) and their initial seated position. This trend can be observed in Table 1. For the 20-mph test, the rider’s body first contacts the vehicle at 185 milliseconds, whereas for the 40mph test, the rider’s body first contacts the vehicle at 55 milliseconds. Thus, it would be the lower speed collisions where the rider’s impact with the car would be most likely to fall outside of the recording window of the EDR. These low-speed collisions may not be of sufficient severity to initiate the recording of an event on the EDR anyway.

In these equations, L is the motorcycle wheelbase reduction in inches, C is the maximum crush to the struck vehicle in inches, and S is the impact speed of the motorcycle in miles per hour. Peck et al reported that, when applied to the full dataset of available tests, these equations had an average error of 0.1 mph with a standard deviation of 5.8 mph. In addition to this error rate, these equations have other limitations. First, they are likely to become more inaccurate as the speed of the struck vehicle increases [Bartlett, 2009].

Second, Peck et al caution against their use when the steering heads, triple clamps, axle mounts, or inner fork tubes are broken. Some of these components were broken on the motorcycles involved in both case studies discussed in this article, so this approach will not be further illustrated in this article. However, the reconstructionist can keep this approach in mind as another option for use in conjunction with the struck vehicle’s EDR data.

Analysis Using Vehicle Damage

In addition to the deformation, another indicator of the motorcycle’s impact speed is the magnitude of the postimpact translation and rotation experienced by the struck passenger vehicle. Simulation can be an effective means to carry out the calculations that relate the motorcycle collision speed to this post-impact translation and rotation of the passenger vehicle. Deyerl and Cheng [2007, 2008] illustrated this type of analysis using EDSMAC4.

Rose [2018, 2019] illustrated this type of analysis using PC-Crash. The use of PC-Crash for this purpose is further illustrated in the case studies covered next. Ogden and Kloberdanz [2012] presented the following equation relating the post-impact angular displacement of the struck vehicle to the impact speed of the motorcycle (equation 7):

In addition to conservation of momentum, reconstructionists can use the wheelbase reduction of the motorcycle and the crush to the struck vehicle to determine the motorcycle speed. Development of this approach began with the study by Severy, et al in 1970 and it has continued to the present day as new staged collisions continue to be added to the literature. For an overview of the literature related to this method, see Rose [2019]. For now, it is sufficient to mention a few key points. First, different regions of the struck vehicle will have different stiffnesses. For example, a fender is typically less stiff than a wheel or axle. Damage analysis yields better results if this fact is considered. To account for it, researchers have typically developed different equations for different regions of the vehicle and they have incorporated the crush to the car into the model (see, for example, Eubanks [1991], Adamson et al [2002], and Bartlett [2009]). Peck, Bartlett, Manning, Dickerson, and Deyerl have made the most recent addition to the literature on this topic, incorporating additional tests and developing the following set of equations [2018]. They parsed the data into four sets, with separate equations for axles,

(3) (4) (5) (6)

Analysis Using the Post-Impact Translation and Rotation of the Struck Vehicle

(7) In this equation, Vmc is the impact speed of the motorcycle, Wmc is the weight of the motorcycle, Wcar is the weight of the struck car, Iy,car is the yaw moment of inertia of the car, lw,car is the wheelbase of the struck car, di is the moment arm of the collision force about the CG of the struck car, α is the post-impact angular displacement of the struck

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car, nµ is an efficiency factor times the roadway coefficient of friction – a term that yields the post-impact drag factor for the struck car.

A number of sources have examined methods for determining the deceleration of a yawing vehicle with one or more wheels that are fully or partially braked due to a driver input or damage [Daily, 2006; Fricke, 2010, Carter et al, 2012; Keifer, 2007]. For example, Fricke discussed the following equation, which was attributed to the CRASH program. In this equation, αslip the tire slip angle or the vehicle sideslip angle, depending on if the equation is being applied to the vehicle as a whole or on a tire-by-tire basis.

The slip angle is likely to vary during the post-impact motion of the struck vehicle, and so, this equation would utilize an average slip angle over the full distance. The braking level is flong, which can vary between 0 and µ. Carter et al compared the speeds calculated with this equation to the measured speeds from the full-scale tests and concluded that this model exhibited accurate physical behavior and yielded calculated speeds close to the measured speeds.

(8) Case Study #1 This section describes the reconstruction of an intersection collision involving a Honda CBR 1000RR motorcycle and a BMW X3 xDrive35i sport utility vehicle (SUV).

EDR data from the BMW was imaged and is incorporated into this reconstruction. This collision occurred on a rural two-lane asphalt roadway. The motorcyclist was riding southbound and the BMW was traveling northbound and attempting to turn left onto a dirt road that intersected

the north-south roadway. The BMW traveled through the path of the motorcycle and the motorcycle struck the front right corner of the BMW, engaging the bumper beam of the BMW and the right front wheel. There was a tire mark on the west shoulder of the roadway from the BMW being diverted by the collision, and this was used to determine the position of the vehicles at impact. The driver of the BMW stated that he saw the motorcyclist approaching, but thought he had time to make the turn. The speed limit for north-south traffic was 60 mph.

The photographs of Figure 2 show the damage to the BMW and the Honda motorcycle along with their rest positions. The bumper beam and the right front wheel of the BMW were directly contacted by the motorcycle.

The photograph of Figure 3 shows the deformation to the bumper beam of the BMW from direct contact with the motorcycle. The right front wheel ultimately broke off of the BMW, but this may have resulted from this wheel impacting the far wall of the ditch where it came to rest. The motorcycle and rider also damaged the right front fender and the hood. As the collision progressed, the rider traveled across the hood of the vehicle and impacted the windshield. The rider’s body came to rest well beyond the rest position of the BMW. Figure 4 depicts the intersection where the collision occurred. The BMW was initially traveling towards the vantage point of the camera, in the lane at the far left of the photograph. The motorcycle was traveling away from the vantage point of the camera, in the lane nearest the center of the photograph. The BMW driver was attempting to turn left into the dirt driveway depicted at the right side of this photograph. The subject intersection was inspected, and its geometry mapped with a Faro scanner (Figure 5).

Figure 2: BMW and Honda Motorcycle at Rest Following the Crash 74

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Figure 3: Deformation to the Bumper Beam of the BMW from Direct Contact with the Motorcycle

Figure 4: Intersection where the crash occurred

Figure 5: Faro Scan Data of the Intersection

Figure 6: Faro Scan Data of the BMW wheel was still situated in the wheel well but was not attached. The steering and suspension linkages for the right front wheel were deformed and broken.

Figure 7: Exemplar BMW Scan with CG and ACM Locations Identified The BMW was also inspected, and its post-collision geometry was documented with a Faro scanner (Figure 6). As this image shows, the right front of the BMW experienced significant deformation. The maximum lateral deformation was approximately 19 inches. At rest, the right front

Manufacturer specifications were obtained for the BMW and the Honda. In addition, an exemplar BMW X3 xDrive35i was inspected, weighed, and tested. The exemplar vehicle was a good match for the subject BMW. Based on the exemplar inspection and manufacturer specifications, we determined that at the time of the subject crash the subject BMW weighed approximately 4,370 lb, including the weight of the driver. From our exemplar inspection, we determined that this vehicle had a total length of 185.5 inches and the CG was approximately 90 inches from the front of the vehicle. The ACM was under the center console and approximately 13.1 inches forward of the CG (Figure 7).

The Honda motorcycle was a CBR 1000RR supersport with a wet weight of approximately 441 lb. The weight of the rider was approximately 170 lb, yielding a total com-

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bined weight of 611 lb. Thus, including the weight of the rider, the vehicles in this collision had a weight ratio of 7.15. The Honda had a standard motorcycle braking system and was not equipped with antilock brakes.

The Bosch Crash Data Retrieval system was used to image data from the airbag control module on the BMW (referred to as the Advanced Crash Safety Module – ACSM –

by BMW). Pre-crash and crash data from one deployment event were recovered. There was 5 seconds of pre-crash data, reported at ½-second intervals (11 total readings).

This data is included in tabular format in Figure 8. At a time of -5.0 seconds, the vehicle speed was reported as 56 km/h (34.8 mph), the brakes were applied, and the vehicle was decelerating. Over the course of the 5 seconds leading up to time zero, the vehicle decelerated at an average rate of approximately -0.14 g and the reported speed at time zero was approximately 19 mph (31 km/h). At time zero, the data indicated that the driver had released the brake and applied the accelerator pedal to 100%, perhaps a part of the BMW driver’s attempt to avoid the collision.

The driver’s steering input was reported as 0 degrees throughout the time from -5.0 to -2.0 seconds. From that point forward, the driver began steering to the left, with the steering input increasing until it reached a maximum reported value of -270 degrees. According to the CDR report, leftward steering inputs were indicated with a negative sign and rightward steering inputs were indicated with a positive sign. The data limitations indicated that the steering angle was reported in 5-degree increments and was limited to a range of -250 to 250 degrees. This is contradicted by the -270-degree reading at time zero.

Nonetheless, it is likely that the steering input reporting does have a maximum value and the signal may have been clipped. Thus, the actual leftward steering input may have exceeded 270 degrees. The BMW had a steering ratio of

17.8:1 and the steering wheel could be turned approximately 1½ rotations in each direction (540 degrees).

PC-Crash simulation software was used to simulate the motion of the BMW in the moments leading up to subject collision. Within this first simulation, the roadway coefficient of friction was set at 0.76 and the TM-Easy tire model was used with default values. The integration time step was set at 1 millisecond. The initial speed of the BMW was set at 34.8 mph (56 km/h), consistent with the EDRreported pre-crash speeds. The steering inputs from the EDR data were used in setting up the sequences within PC-Crash, with the exception that the steering input at time zero was not limited to -270 degrees. This ending steering input was allowed to exceed a magnitude of 270 degrees as dictated by the geometry of the turn and the location of physical evidence from the collision. The steering input was optimized so that the BMW arrived at its impact position as dictated by this physical evidence.

In simulating this crash, it was recognized that the time zero speed reading was likely just the last sampled speed prior to the collision. This speed could have been sampled anytime between 0 and 0.4999 seconds prior to the collision, and so, time zero in the pre-crash data could be the time of the collision or it could be as much as a half second prior to the collision. The EDR data also indicates that the driver was inputting 100% throttle at time zero. To evaluate the effect of the uncertainty around the exact timing of the 31 km/h (19.3 mph) pre-crash speed reading, we tested an exemplar BMW X3. The maximum acceleration in this speed range was around 0.5 g. At this acceleration, the speed of the BMW could increase by approximately 5.5 mph (8.9 km/h) over ½ second. Thus, the speed of the BMW at the time of the collision could be anywhere between 19.3 and 24.8 mph (31 and 40 km/h).

Figure 8: Pre-crash data from the BMW’s ACSM 76

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Sequences in PC-Crash were used to apply deceleration to the vehicle to match the EDR-reported speeds throughout the 5 seconds of reported data. It took the BMW approximately 5¼ to 5½ seconds in the simulation to reach the area of the collision. This is consistent with the discussion of the previous paragraph and implies that time zero in the pre-crash data was not coincident with the time of the collision. In the simulation, the BMW arrived at the point of impact at a speed in the neighborhood of 22 mph. Figure 9 is a screen capture from PC-Crash showing the pre-crash motion of the BMW. The depicted positions are at ½-second intervals. Figure 10 is a graph that compares the EDR-reported pre-crash speeds to the BMW speeds in the simulation. In this graph, time is plotted on the horizontal axis and speed on the vertical axis. The EDR-reported speeds are depicted with blue circles and the speeds from the PC-Crash simulation are plotted with a black line.

Figure 9: Screen Capture from PC-Crash showing Pre-Collision Motion of BMW (Depicted Positions are at ½-second Intervals)

Once the left turn of the BMW was simulated, a separate PC-Crash file was setup to simulate the collision. The speed of the BMW at impact was initially set at 22 mph and the vehicles were aligned consistent with the damage. A brake factor of 100% was used for the right front wheel of the BMW, 10% for the right front, and 5% for the rear wheels. The goal of this simulation was to determine the impact speed of the motorcycle that would produce a match with the tire mark deposited by the BMW, the rest position of the BMW, and the ΔV of the BMW. The CDR report indicated that the BMW experienced a maximum longitudinal ΔV of 9.9 mph (16.0 km/h), oriented from front-to-rear, and a maximum lateral ΔV of 6.8 mph (11.0 km/h), oriented from right-to-left. These ΔV components imply a PDOF of 34.5 degrees, measured clockwise relative to the front of the vehicle. The reported maximum resultant ΔV was approximately 12.1 mph (19.5 km/h). The maximum recorded longitudinal ΔV was achieved at 80 milliseconds, the maximum recorded lateral ΔV was achieved at 48 milliseconds, and the maximum resultant ΔV was achieved at 82 milliseconds. The longitudinal cumulative ΔV graph is included in Figure 11. The lateral cumulative ΔV graph is included in Figure 12. The longitudinal and lateral acceleration graphs from the CDR report are included as Figure 13 and Figure 14. The

Figure 10: PC-Crash Pre-Collision Speeds Plotted with the EDR-Reported Pre-Crash Speeds

Figure 11: Longitudinal ΔV Data from ACSM www.collisionpublishing.com

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longitudinal acceleration peaks at -11 g, at a time between 30 and 40 ms. That lateral acceleration also peaks at -11 g, at a time between 40 and 50 milliseconds.

Figure 12: Lateral ΔV Data from ACSM

Figure 13: Longitudinal Accelerations from the ACSM

Figure 14: Lateral Accelerations from the ACSM 78

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The cumulative ΔV and acceleration curves all report readings of 0 for the first 10 ms. This seems to be an indication that the ACSM is not missing the early portions of the collision because of the need for the accelerations to exceed a threshold.

This interpretation is consistent with the fact that the CDR report indicates a “time from time zero to algorithm wake-up start” for the frontal event of 0 milliseconds and for the side event of 4 milliseconds. The ACSM’s ability to report this seems to be evidence that the system is storing some of the accelerations just prior to the collision.

Thus, error source #1 can be ruled out in this instance. The reporting window in this instance is 300ms. This recording window was more than adequate to capture the peak accelerations and ΔVs. Error source #2 can be ruled out. In addition, tire and dragging forces that may be occurring for the BMW later in the 300 ms window are not resulting in a new maximum ΔV. So, error source #3 can be ruled out.

Based on the acceleration curves, the impact duration appears to be approximately 80 ms. With a 12-mph ΔV and an 80 ms impact duration, the average acceleration would be approximately 6.8 g. Peak accelerations of 11 g make good sense with this average acceleration. Beyond that, the reported peak accelerations are well below those that would typically result in clipping (40 to 50 g). Error source #4 can be ruled out. The CDR report indicated that recording the event was completed. We were able to communicate with the ACSM through the DLC and no deformation to the center console was evident during our inspection of the subject vehicle. Error source #6 can be ruled out. The recorded accelerations appear to have captured the entire crash event, and the EDR reported the complete file was recorded, so error source #7 can also be ruled out.

Now, consider error source #5. The ΔVs from the CDR report were all measured at the location of the ACM, which was under the center console on this vehicle, approximately 13.1 inches forward of the CG. During the subject crash, the BMW did experience approximately 90 degrees of yaw rotation. The ΔV experienced at the CG of the BMW can be related to the ΔV experienced at the ACM with the following equation: (9) In this equation, ΔVACM is the vector ΔV at the ACM, ΔVCG is the vector ΔV at the CG, Δyaw is the change in rotational velocity about the vehicle’s vertical axis, and rACM/ is the vector connecting the ACM position to the CG. CG This equation neglects changes in pitch and roll velocity experienced by the vehicle and discrepancies between the vertical position of the CG and the ACM. Evaluating the cross product in Equation (9) and breaking it down into longitudinal and lateral components yields the following equations: (10) (11) In these equations, rx and ry are the coordinates of the ACM within the vehicle coordinate system with its origin at the CG. Within this coordinate system, a positive value of rx would imply that the ACM is located forward of the center of gravity. A positive value of ry would imply that the ACM is situated to the right of the CG. Often, both the CG and the ACM will be on the centerline of the vehicle, and ry will be equal to 0. If this is the case, then Equations (10) and (11) can be simplified to the following: (12) (13) A positive value for Δyaw implies a clockwise rotation and a negative value implies a counter-clockwise rotation. In this specific case, rx= 13.1 inches or 1.09 feet. The change in angular velocity that the BMW experienced had a counterclockwise direction (a negative sign). This means that, in this instance, the lateral ΔV experienced at the CG of the BMW will be of lower magnitude than what the ACM experienced and reported. Based on initial simulation efforts, it was clear that the change in yaw velocity of the BMW would have to be around 100 degrees per second in order to match the rest position. Using this Δω to correct the EDR-reported ΔVs results in the lateral ΔV at the CG being -5.5 mph, instead of the -6.8-mph reported by the EDR. The resultant ΔV at the CG would be 11.3 mph, instead of the 12.1 mph ΔV at the ACM. These values can

be substituted into Equation (1), to illustrate the error that would result if this correction was not made: (14) (15) Thus, not accounting for the spin of the vehicle and the discrepancy in position between the ACM in the CG could result in a 5.7 mph error in the motorcycle’s ΔV, and thus, in its impact speed. In these calculations, the full weight of the rider has been included, which assumes that the full weight of the rider participated in the collision. This is a conservative assumption, resulting in the lowest speed for the motorcycle since a lower weight for the Honda would result in a higher weight ratio between the two vehicles.

The calculations of the previous paragraphs corrected for the spin of the vehicle using the as-reported ΔV components. However, we have not yet considered the uncertainty due to the potential error in the EDR-reported ΔV components. In order to carry out this uncertainty analysis, Monte Carlo simulation was used [Kost, 1994; Wood, 1994; Rose, 2001; Bartlett, 2003; Fleck, 2010]. The software Crystal Ball by Oracle was utilized to implement this Monte Carlo analysis. The longitudinal ΔV was varied between -9.9 and -11.4 mph using a uniform distribution.

The lateral ΔV was varied between -5.3 to -8.3 mph using a uniform distribution. When combined with analysis using Equations (11) and (12), this led to a range on the corrected lateral ΔV between -4.0 and -7.0 mph. This in turn led to a range on the corrected resultant ΔV of 12.0 ± 0.6 mph and the corrected PDOF of 27.2 ± 3.8 degrees.

This means that, in this specific instance, the effects of the potential error in the EDR-reported ΔV offset the effects of the spin and the discrepancy in position between the ACM and the CG. This will not always be the case and these factors will need to be considered on a case-by-case basis. Table 2 summarizes the as-reported and corrected ΔVs for this case.

The simulation was first optimized to match the corrected resultant ΔV obtained from the as-reported ΔV components, without consideration of the uncertainty in these components. This will result in a low-end estimate of the motorcycle impact speed. This optimization was carried out using the impact speed of the motorcycle, along with the coefficient of restitution and the impact center height.

Small changes in the impact speed of the BMW were also utilized for the final step in the optimization, and it was found that an impact speed of 21 mph for the BMW resulted in an improved match with the evidence. Ultimately, an impact speed for the motorcycle of 76 mph pro-

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As-Reported Longitudinal ΔV (mph) Lateral ΔV (mph) Resultant ΔV (mph)

-9.9 -6.8 12.1

Corrected for ACM Location -9.9 -5.5 11.3

Low End in Uncertainty Analysis -9.9 -4.0 11.4

High End in Uncertainty Analysis -11.4 -7.0 12.6

Table 2: Summary of Uncorrected and Corrected ΔVs duced a match with the corrected EDR-reported ΔV (11.3 mph), the tire mark, and the rest position of the BMW. The screen capture in Figure 15 shows the impact configuration, the rest positions, and the “Crash Simulation” dialogue box within PC-Crash that reports the impact speeds and the calculated ΔVs. The green box in the screen capture represents the actual rest position of the BMW. This screen capture shows that a reasonable match with the BMW’s actual rest position as achieved. In this simulation, the BMW had a PDOF of 31.8 degrees, a value within 2.7 degrees of the value obtained with the corrected ΔV components.

The simulation was first optimized to match the corrected resultant ΔV obtained from the as-reported ΔV components, without consideration of the uncertainty in these components. This will result in a low-end estimate of the motorcycle impact speed. This optimization was carried out using the impact speed of the motorcycle, along with the coefficient of restitution and the impact center height.

Small changes in the impact speed of the BMW were also utilized for the final step in the optimization, and it was found that an impact speed of 21 mph for the BMW resulted in an improved match with the evidence. Ultimately, an impact speed for the motorcycle of 76 mph produced a match with the corrected EDR-reported ΔV (11.3 mph), the tire mark, and the rest position of the BMW. The screen capture in Figure 15 shows the impact con-

Figure 15: PC-Crash Screen Capture of Low-End Speed Optimized Impact Simulation 80

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figuration, the rest positions, and the “Crash Simulation” dialogue box within PC-Crash that reports the impact speeds and the calculated ΔVs. The green box in the screen capture represents the actual rest position of the BMW. This screen capture shows that a reasonable match with the BMW’s actual rest position as achieved. In this simulation, the BMW had a PDOF of 31.8 degrees, a value within 2.7 degrees of the value obtained with the corrected ΔV components.

The high-end motorcycle speed estimate can be obtained by using a BMW resultant ΔV of 12.6 mph. A second simulation was optimized to match this value. In this instance, it was found that an impact speed of 23 mph for the BMW and an impact speed for the motorcycle of 84 mph resulted a quality match with the BMW rest position and the 12.6 mph ΔV. The screen capture in Figure 16 shows the impact configuration, the rest positions, and the “Crash Simulation” dialogue box within PC-Crash that reports the impact speeds and the calculated ΔVs. In this simulation, the BMW again had a PDOF of 31.8 degrees. The rest position of the simulated BMW agreed very closely with the actual rest position of the BMW (again represented with the green box).

Thus, these two simulations produced a speed range for the motorcycle of 76 to 84 mph. Because the full weight of the rider was included in the simulation, this speed range

Figure 16: PC-Crash Screen Capture of HighEnd Speed Optimized Impact Simulation

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is conservative. In reality, the velocity direction of the motorcycle in this crash was redirected by approximately 90 degrees, whereas the rider continued essentially along his initial direction of travel. The rider did interact with the BMW, but not to the extent that he was redirected along with the motorcycle. In an instance like this, it would be reasonable to use ¼ to ½ of the rider’s weight in the simulation. Doing so would increase the calculated speed of the motorcycle significantly. We carried out these calculations and found that using half of the rider’s weight increased the range on the impact speed of the motorcycle to between 88 and 96 mph, and increase of approximately 12 mph in each end of the range.

Case Study #2 This section describes the reconstruction of another intersection collision involving a Harley-Davidson motorcycle and a Ford Edge. As with the previous case study, EDR data from the Ford was available and is incorporated into the reconstruction. This collision involved a motorcyclist riding eastbound through the subject intersection. The driver of a westbound Ford attempted to turn left to go southbound and the motorcycle struck the passenger’s side rear of the Ford. The driver of the Ford Edge stated that she did not see the motorcycle prior to initiating her turn on a yellow traffic signal. A witness who observed the motorcycle prior to the collision stated that the motorcyclist accelerated into the intersection to make it through on the yellow light. The speed limit for east-west traffic through the intersection was 60 mph. An investigating officer noted that sun glare could have been a factor contributing to the Ford driver not seeing the motorcyclist.

and shows the damage to the vehicle from the collision.

The motorcycle struck and damaged the passenger’s side rear of the Ford, directly contacting the rear wheel behind the axle. This caused the wheel to deform and rotate clockwise relative to the vehicle and the tire to go flat. The collision caused significant clockwise yaw rotation of the Ford, on the order of 180°, and the Ford deposited prominent tire marks between impact and rest. Figure 18 also shows that the front tires of the Ford are steered to the left following the collision.

The subject intersection was inspected, and its geometry mapped with a Faro Focus3D X 330 scanner. Some of the resulting scan data is depicted in Figure 19. The Ford was also inspected, and its post-collision geometry documented with the same scanner. The resulting scan data for this vehicle is depicted in Figure 20. The clockwise rotation of the passenger’s side rear wheel due to deformation was measured from this scan data at 17° relative to the vehicle.

In the photographs of the Ford at rest in the intersection, the leftward steer angle of the right front wheel appears to be of greater magnitude than the 17° the right rear wheel was angled.

The photograph in Figure 17 depicts the motorcycle and the Ford at rest in the intersection following the collision.

The photograph in Figure 18 also depicts the Ford at rest

The Bosch Crash Data Retrieval system was used to image data from both the restraint control module (RCM) and the powertrain control module (PCM) on the Ford.

Data from one event was recovered from the RCM – a side deployment event. The data indicated that the driver pretensioner and both the driver and passenger curtain airbags deployed 28 milliseconds after the system began sensing the collision. The data also indicated that the driver, who was the sole occupant of the vehicle, was buckled at the time of the collision. The RCM data included pre-crash data at 5 indicated time intervals (-4 s, -3 s, -2 s, -1 s, and 0 s). This data is included in Figure 21. Finally, the RCM data reported a lateral ΔV of 2.49 mph.

Figure 17: Motorcycle and Ford Edge at rest in the intersection

Figure 18: EDR-equipped Ford Edge involved in the subject collision

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Figure 19: Faro laser scan data for the accident site

Figure 20: Faro laser scan data for the Ford

The reporting window for the lateral ΔV was only 50 milliseconds. Based on the timing reported for the crash tests reported by Severy, this recording window would be too short to capture the entire collision between the Ford and the motorcycle and certainly too short to capture the collision between the rider and the Ford (error source #2). The graphical data for this lateral ΔV is depicted in Figure 22.

As this figure shows, the horizontal axis of the graph starts at minus 49 milliseconds and goes to 0 milliseconds. The first non-zero acceleration reading is at -28 milliseconds, so in this instance the EDR record was even shorter than the already short recording window. Given that the system reports that the curtain airbags deployed 28 milliseconds after the collision began being sensed, it seems likely that, in this case, the EDR only reported the timeframe from the start of the collision until the deployment of these airbags. Given this, the ΔV reported by the EDR will significantly under-report the actual ΔV experienced by the Ford during the collision, and this is an instance where the EDR-reported ΔV cannot be used in the reconstruction.

nificantly removed from the area of the vehicle that was struck, and so, it is unlikely that the collision caused any deformation to the RCM or its surrounding structure.

There was a discrepancy between the CG of the vehicle and the location of the RCM and the collision did cause significant rotation of the Ford. Specifically, the CG of this vehicle was 83.9 inches from the front of the vehicle, whereas the ACM was approximately 75 inches from the front of the vehicle. Thus, the ACM was approximately 8.9 inches in front of the CG. If the ΔV had been useable in this instance, there would have been a need to make a correction for this discrepancy.

Given this, there is not necessarily a need to evaluate the other potential error sources. Nonetheless, for the sake of illustration, we can observe first that the RCM was sig-

Data was also recovered from the PCM on the Ford. The graphical data, which is depicted in Figure 23, included the vehicle indicated speed, the accelerator pedal percentage, brake switch status, anti-lock brake status, and engine rpms for 20 s prior to and 5 s after time zero. Tabular data was also reported for each of the variables included in the graph. In addition, the tabular data added transmission status (reverse or not reverse), speed control status (on or off), traction control status (active or inactive), and stability control status (active or inactive). According to the data limitations in the CDR report, time zero corresponds to

Figure 21: Pre-crash data from the Ford’s RCM 82

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Figure 22: Lateral velocity change data from the Ford’s RCM the time at which the PCM received a restraint deployment signal from the RCM. However, “The Restraint Deployment Signal (RDS) may not be recorded on the PCM immediately at impact. Time lags within the system may result in the Restraint Deployment Signal being recorded a few data samples after impact has occurred.” The SUV was equipped with electronic stability control and the tabular data from the PCM indicates that it was activated from +0.2 s through +1.4 s. After that, it was inactive. The AdvanceTrac system utilizes the ABS system and the data also reports the ABS system was active during the same timeframe.

Arndt et al [2009] reported a study of the accuracy of the speeds reported by the PCM on a 2005 Ford Explorer during high sideslip angle maneuvers and during acceleration and braking. This test vehicle was equipped with electronic stability control system (ESC). Tests were conducted with and without the ESC active. Arndt noted that “in testing with ESC enabled, speed error was associated with ESC intervention … A Ford PCM download which provides recorded speed every 0.2 seconds appears to provide enough data that an accurate speed trend can be discriminated.”

For the subject collision, ESC intervention did occur, but not until after the collision. The tabular data identifying this ESC-intervention is included in Figure 24. The second to last column states that the ESC was intervening from +0.2 s to +1.4 s. Based on Arndt’s results, there would likely be intervention-related error in the reported speeds during this timeframe. In addition to that error source, the significant yaw rate of the Ford following the collision would be expected to result in errors in the reported speeds because of the discrepancy between the heading and velocity directions of the vehicle.

That said, the speeds reported by the PCM prior to the collision are likely to be accurate. These speeds were recorded when the vehicle would have a low sideslip angle and when there was no ESC-intervention. Ruth et al [2008] reported testing to evaluate the accuracy of PCM-reported speeds under steady state conditions. They compared PCM-reported speeds to speeds measured with a 100-Hz VBOX and with a speed trap and found a maximum difference of 0.61 km/h (0.38 mph).

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Figure 23: PCM data from the SUV The tabular data indicated that the speed of the Ford at time zero (the time of the restraint deployment signal) was 35 km/h (21.7 mph). This data also indicated that the vehicle was accelerating in the seconds leading up to the collision. The data from the RCM, on the other hand, reported a speed at time zero of 27.9 km/h (17.3 mph). This is 4.4 mph lower than the speed reported by the PCM for time 0. The RCM and the PCM are obtaining their speeds from the same sensor – a speed sensor on the transmission output shaft. However, they are sampling from this sensor at different rates. The PCM obtains a speed reading every 0.2 s, whereas the RCM obtains a reading every 1 second. The speed reported by the RCM for time 0 would simply be the last speed reading obtained by the RCM prior to the collision. Thus, the RCM did not capture the actual speed at the time of the collision, rather it captured a speed sometime in the 1-second time window preceding the collision. Looking at the tabular data from the PCM, the 17.3 mph speed reported by the RCM would have been measured sometime between -0.6 and -0.4 s in the PCM data. For the analysis reported here, the collision speed was estimated from the PCM data.

Camera-matching photogrammetry was utilized to locate the vehicle rest positions, tire marks from the Ford, and scrapes and gouges on the asphalt from the motorcycle [see Rose, 2019 for an overview of the literature related to this technique]. Camera matching involves reconstructing the location and characteristics of the camera that took the photograph being analyzed. Once the camera location and characteristics are obtained, physical evidence within

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the photograph can be located. This technique involves the following steps: 1. A photograph is selected for analysis.

2. The scene geometry depicted in the photograph is mapped.

3. The mapping data is imported into a computer modeling software package and viewed using a virtual camera with a vantage point similar to that shown in the photograph.

4. Lens distortion is removed from the photograph.

5. The corrected photograph is then imported into the modeling software and is designated as a background image for the virtual camera.

6. Adjustments are then made to the location, focal length, and viewing plane of the virtual camera until an overlay is achieved between the mapping data and the scene geometry shown in the photograph. Once a match is obtained, then the camera has been reconstructed.

7. Once the camera location and characteristics have been obtained, the evidence visible in the photograph can be located.

This process was carried out for several police photographs that depicted evidence related to the subject collision. A sample of these photogrammetry results is included in the following figures. Figure 25 is one of the police photographs that depicts the tire marks deposited by the Ford following the collision. Figure 26 shows the scan data from the accident site overlaid on this photograph, in-

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Figure 24: Tabular data from the PCM showing ESC intervention

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Figure 25: Police photograph selected for analysis

Figure 26: Scan data overlaid onto the police photograph via camera matching

Figure 27: Physical evidence located in the photograph 86

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Figure 28: Motion from optimized simulation dicating that the camera location and characteristics have been reconstructed. Figure 27 shows the tire marks located and traced with dark blue outlines. A set of scrapes and gouges on the asphalt are also shown in this image (with light blue lines) that were traced from another police photograph that was also analyzed.

PC-Crash simulation software was used to simulate the subject collision, specifically to determine the motorcycle impact speed necessary to cause the documented post-collision rotation of the Ford. In setting up this simulation, manufacturer specifications were obtained for each of the vehicles to obtain the geometric dimensions and weights.

The Ford was equipped with a 3.5L, V6 gasoline engine, an automatic transmission, all-wheel drive, anti-lock brakes, and ESC. Based on the manufacturer specifications for this vehicle, the weight at the time of the collision was estimated at 4,385 lb, including the weight of the driver. The motorcycle was equipped with a 1584cc V-Twin engine, a manual transmission, and a conventional braking system.

The weight of the motorcycle at the time of the collision was approximately 714 lb and the weight of the rider was approximately 170 lb. The rider’s weight was included in the motorcycle’s weight within the simulation.

In the simulation of this collision, the roadway coefficient of friction was set at 0.76 and the TM-Easy tire model was used with default values for both vehicles. The integration time step was set at 5 milliseconds. Because the motorcycle struck the passenger’s side rear wheel of the Ford, the coef-

ficient of restitution was set at 0.25, consistent with the coefficients of restitution for other wheel impacts reported

by Rose [2019]. Based on the EDR data, the impact speed of the SUV was initially set at 21.7 mph. Sequences in PCCrash were used to steer the passenger’s side rear wheel of the Ford 17° to the right immediately following the collision. The simulation was optimized using the impact speed of the motorcycle, the precise location of the collision on the roadway, the intervehicular friction, the impact center height, the steering angles of the front wheels, and the brake factors for each wheel. Small changes in the impact speed of the Ford were also utilized for the final step in the optimization. Consistent with the orientation of the front wheels of the Ford when it was at rest in the intersection, leftward steering inputs of 23° at the left front wheel and 19° at the right front wheel were ultimately utilized, developing over 2.3 s. The Ford was an all-wheel drive vehicle, and brake factors of 5% were used for the front wheels and the left rear wheel. A brake factor of 100% was used for the wheel that was struck and deformed by the motorcycle – the right rear wheel. The exception to these brake factors was during the interval of ESC-intervention, during which a 100% brake factor was assigned to the left front wheel, and when the system indicated that the driver applied the brakes near the rest position. Brake factors were not applied to the wheels of the motorcycle because it fell on its side shortly after the collision. The coefficient of friction between the body of the motorcycle and the ground was ultimately set at 0.4, though this parameter was also iterated in optimizing the simulation.

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Figure 28 is a screen capture from PC-Crash showing the final impact parameters and the motion of the vehicles following the collision. A high-quality match was obtained with the rest positions for both vehicles and the Ford’s postcollision motion matched very well with the documented tire marks. The optimization process led to the conclusion that the motorcycle was traveling 69 mph at the time of the collision, 9 mph greater than the speed limit. During the optimization process, it was recognized that the overall rotation of the Ford could be matched with a wide range of motorcycle impact speeds. However, the rate of rotation, as represented by the tire marks that were deposited, could only be matched with a narrow band of motorcycle impact speeds. In this case, a high-quality match was obtained with the motorcycle rest position, though the trajectory of the motorcycle was slightly below the documented scrapes and gouges. Such a quality match of the motorcycle’s rest position would not be expected in every case, but often matching at least the post-impact velocity direction of the motorcycle will help with the simulation optimization.

The change in velocity calculated for the Ford in this simulation was 10.7 mph with a principal direction of force of 81 degrees. Thus, the total lateral velocity change for the SUV was approximately 10.6 mph, 8.1 mph more than what was reported by the RCM. The simulation calculated a change in yaw rotational speed of the SUV of approximately 280 degrees per second.

Discussion and Conclusions Both pre-crash and crash data from the EDR on a vehicle involved in a collision with a motorcycle can be used within the reconstruction. However, this data will have potential limitations that the reconstructionist may need to take into account. For example, if the EDR from the struck vehicle reports the ΔV, this ΔV can be used to calculate the impact speed of the motorcycle. However, because of the large weight discrepancy that may be present between the motorcycle and the struck vehicle, this approach can be sensitive to errors or uncertainties in the EDR-reported ΔV. Seven specific error sources have been discussed in this and the companion article. The more of these error sources that can be ruled out or corrected for, the better.

The companion article concluded that, if these seven error sources can be ruled out or corrected for, then a reasonable range on the actual longitudinal ΔV for the struck vehicle would have a low-end of the ΔV reported by the EDR and a high-end of 1.5 to 2 mph greater in magnitude than what is reported by the EDR. A similar range could be applied to the EDR-reported lateral component of the ΔV, though perhaps the range should be centered on zero (±1 mph, for instance). The case studies in this 88

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article have demonstrated that another important factor to consider is a discrepancy in position between the ACM and the CG, particularly for instances where the impact causes significant rotation of the struck vehicle. This is one of the seven error sources, but it is one that need not increase the error range on the EDR-reported ΔV since it can be evaluated and corrected for within the reconstruction.

In the end, the actual resultant ΔV could be either greater than or less than the EDR-reported ΔV and the analyst should consider potential error sources on a case-by-case basis. In addition to that, analysis of the EDR data can be combined with analysis using the combined deformation to the struck vehicle and the motorcycle or using the post-impact translation and rotation of the struck vehicle.

The motorcycle impact speed that the reconstructionist ultimately reports can consider the results from each of these methods.

References 1. Adamson, Kelley S., “Seventeen Motorcycle Crash Tests into Vehicles and a Barrier,” Society of Automotive Engineers Technical Paper Number 2002-010551.

2. Arndt, M.W., Rosenfield, M., Stevens, D., Arndt, S., “Test Results: Ford PCM Downloads Compared to Instrumented Vehicle Response in High Slip Angle Turning and Other Dynamic Maneuvers,” SAE Technical Paper 2009-01-0882, 2009, doi:10.4271/200901-0882.

3. Bartlett, W., “Conducting Monte Carlo Analysis with Spreadsheet Programs,” SAE Technical Paper 200301-0487, 2003, https://doi.org/10.4271/2003-010487.

4. Bartlett, W., “Motorcycle Crush Analysis,” Accident Reconstruction Journal, March/April 2009, pp. 2529, ISSN 1057-8153.

5. Beck, R., Casteel, D., Phillips, E., et al., “Motorcycle Collinear Collisions Involving Motor Vehicles Equipped with Event Data Recorders,” Collision Magazine 1(1): 82-96, 2006.

6. Bortles, W., Biever, W., Carter, N., and Smith, C., “A Compendium of Passenger Vehicle Event Data Recorder Literature and Analysis of Validation Studies,” SAE Technical Paper 2016-01-1497, 2016, https:// doi.org/10.4271/2016-01-1497.

7. Bundorf, R.T., “Analysis and Calculation of Delta-V from Crash Test Data,” SAE Technical Paper 960899, 1996, https://doi.org/10.4271/960899.

8. Carter, N., Beauchamp, G., Rose, N.A., “Comparison of Calculated Speeds for a Yawing and Braking Vehicle to Full-Scale Vehicle Tests,” SAE Technical Paper 2012-01-0620, 2012, doi:10.4271/2012-01-0620.

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9. Daily, John, Shigemura, Nathan, Daily, Jeremy, Fundamentals of Traffic Crash Reconstruction, Institute of Police Technology and Management, 2006.

10. Deyerl, Eric, Cheng, Louis, “Computer Simulation of Staged Motorcycle-Vehicle Collisions Using EDSMAC4,” white paper presented at the 2008 HVE Forum, HVE-WP2008-3.

11. Deyerl, Eric, Cheng, Louis, “Computer Simulation of Staged Motorcycle-Vehicle Collisions Using EDSMAC4,” Accident Reconstruction Journal, July/August 2007.

12. Eubanks, J., “Motorcycle Speed-from-Damage Estimates Update,” Society of Accident Reconstruction, pp. 22, October 1991.

13. Fatzinger, E., Landerville, J., “An Analysis of EDR Data in Kawasaki Ninja 300 (EX300) Motorcycles,” SAE Technical Paper 2017-01-1436, 2017, doi:10.4271/2017-01-1436.

14. Fatzinger, E., “An Analysis of EDR Data in Kawasaki Ninja ZX-6R and ZX-10R Motorcycles Equipped with ABS (KIBS) and Traction Control (KTRC),” SAE Technical Paper 2018-01-1443, 2018, doi:10.4271/2018-01-1443.

15. Fleck, G. and Daily, J., “Sensitivity of Monte Carlo Modeling in Crash Reconstruction,” SAE Int. J. Passeng. Cars – Mech. Syst. 3(1):100-112, 2010, https:// doi.org/10.4271/2010-01-0071.

16. Fricke, Lynn B., Traffic Crash Reconstruction, Second Edition, Northwestern University Center for Public Safety, 2010.

17. Haight, S., Haight, R., “Analysis of Event Data Recorder Delta-V Reporting in the IIHS Small Overlap Crash Test,” Collision 8(2): 8-23, 2013.

18. Keifer, Orion, Richard Conte, and Bradley Reckamp, Linear and Rotational Motion Analysis in Traffic Crash Reconstruction, Institute of Police Technology and Management, 2007.

19. Kost, G. and Werner, S., “Use of Monte Carlo Simulation Techniques in Accident Reconstruction,” SAE Technical Paper 940719, 1994, https://doi. org/10.4271/940719.

20. Marine, M.C., Werner, S.M., “Delta-V Analysis from Crash Test Data for Vehicles with Post-Impact Yaw Motion,” SAE Technical Paper 980219, 1998, https:// doi.org/10.4271/980219.

21. McNally, Bruce F., Bartlett, Wade, “Motorcycle Speed Estimates Using Conservation of Linear and Rotational Momentum,” 20th Annual Special Problems in Traffic Crash Reconstruction, IPTM, Jacksonville, FL, April 15-19, 2002.

22. Niederer, P.F., “Some Aspects of Motorcycle-Vehicle Collision Reconstruction,” SAE Technical Paper 900750, 1990, doi:10.4271/900750.

23. Ogden, J.S., Kloberdanz, K.M., “Forensic Engineering Analysis of Motorcycle Impacts Using Rotational Mechanics and Fork/Vehicle Deformation,” NAFE 561F/430C, National Academy of Forensic Engineers, June 2012.

24. Peck, Louis, Bartlett, Wade, Manning Joseph, Dickerson, Charles, Deyerl, Eric, “Eleven Instrumented Motorcycle Crash Tests and Development of Updated Motorcycle Impact-Speed Equations,” SAE Technical Paper 2018-01-0517, 2018, https://doi. org/10.4271/2018-01-0517.

25. Rose, N., Fenton, S., and Hughes, C., “Integrating Monte Carlo Simulation, Momentum-Based Impact Modeling, and Restitution Data to Analyze Crash Severity,” SAE Technical Paper 2001-01-3347, 2001, https://doi.org/10.4271/2001-01-3347.

26. Rose, Nathan A., Beauchamp, Gray, Bortles, Will, “Quantifying the Uncertainty in the Coefficient of Restitution Obtained with Accelerometer Data from a Crash Test,” SAE Technical Paper 2007-01-0730, 2007, https://doi.org/10.4271/2007-01-0730.

27. Rose, Nathan A., Neal Carter, Gray Beauchamp, “Post-Impact Dynamics for Vehicles with a High Yaw Velocity,” SAE Technical Paper Number 2016-011470, https://doi.org/10.4271/2016-10-1470.

28. Rose, Nathan A., Neal Carter, “An Analytical Review and Extension of Two Decades of Research Related to PC-Crash Simulation Software,” SAE Technical Paper 2018-01-0523, 2018, https://doi.org/10.4271/201801-0523.

29. [Rose, 2019a] Rose, Nathan, Neale, William, Motorcycle Accident Reconstruction, SAE International, 2019.

30. [Rose, 2019b] Rose, N., Bortles, W., Carter, N., Randolph, M., McDonough, S., “Motorcycle Accident Reconstruction: Applicable Error Rates for Struck Vehicle EDR-Reported V,” forthcoming from Collision, 2019.

31. Ruth, R.R., West, O., Engle, J., Reust, T.J., “Accuracy of Powertrain Control Module (PCM) Event Data Recorders,” SAE Technical Paper 2008-01-0162, 2008, doi:10.4271/2008-01-0162.

32. Severy, D., Brink, H., and Blaisdell, D., “Motorcycle Collision Experiments,” SAE Technical Paper 700897, 1970, https://doi.org/10.4271/700897.

33. Strohmeyer, Ken, “Motorcycles: Myths, Methodologies, and Momentum,” IPTM Symposium on Traffic Safety, May 21-24, 2018, Orlando, Florida.

34. Wood, D. and O’Riordain, S., “Monte Carlo Simulation Methods Applied to Accident Reconstruction and Avoidance Analysis,” SAE Technical Paper 940720, 1994, https://doi.org/10.4271/940720.

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Tesla EDR Case Studies & Reconstruction Techniques Weston Brown & Robert D Anderson

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bstract Tesla is currently the newest addition to the nearly comprehensive family of EDR/CDR supported vehicles. Aspects of the technology that are unique to this manufacturer will be covered through comprehensive review of case studies of crashes involving Tesla vehicles. Besides the mechanics of obtaining a download and telematics information, the discussion will include how data from the vehicle telematics obtained through Tesla compares and differs from the data retrieved from the airbag control module, as well as traditional reconstruction methods as well as video evidence evaluation.

Introduction In today’s ever more technological world, we are increasingly offered different avenues to arrive at vehicle speeds.

In 16 short years Tesla has made a significant impact on the automotive industry. According to Forbes, Tesla Model S and X made up 45% of the sales of electric vehicles in the United States and according to Statista by December of 2018 Tesla held 2.08% of the U.S. market share of cars and light trucks. They are a leader in new technology in vehicles and beginning with the Model S look at diagnostic data directly from the vehicle on a regular basis. This diagnostic data was generally only available to law enforcement on presentation of proper authority, i.e. a search warrant. In 2018 Tesla released its Event Data Retrieval Tool during the 2018 EDR Summit in Houston, Texas. This tool allowed for private consultants to retrieve information relevant to a crash and complied with 49CFR563. This initial rollout of the Tesla EDR tool included legacy coverage of: Model S, 2012 to present; Model X, 2016 to present; and Model 3, 2017 to present. The only model not included in coverage was the Roadster, produced from 2002 to 2012 prior to the effective date of 49CFR563.

Tool Overview The Tesla EDR retrieval tool continues Tesla’s innovation by utilizing automated in-house data translation.

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The software is free and available from Tesla’s EDR Reporting Service Portal. The retrieval equipment can be purchased through Crash Data Group and includes the following components: •

Hard-Shell protective carrying case with padded dividers, plus

•

TPCAN: PCAN-USB adapter

•

Power: AC power supply unit

•

TIV-144: Model S and Model X

•

TIV-145: Model S older than Sept. 2016

•

TIV-996: Model 3

•

TD2M-139: Model S

•

TD2M-601: Model X and 3

•

TD2M-602: Model S older than mid-2019

The Tesla EDR retrieval tool currently includes the PCAN interface, which is analogous to the Bosch CDR Tool’s CAN Plus or CDR 900 interfaces, and the Tesla In-Vehicle (TIV) cables and the Tesla Direct-to-Module (TD2M) cables, are analogous to the Bosch CDR Tool’s DLC, and direct-tomodule cables.

Figure 1: Tesla EDR Kit

The process for retrieving the raw data is similar to the Bosch CDR Tool (Figure 2). The software must be obtained and installed on a windows computer. Once the PCAN drivers are properly installed, the raw *.edr file is obtained via either an in-vehicle connection or direct to module connection, whichever is appropriate for the vehicle’s condition.

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Download and install EDR retrieval software Purchase hardware from Crash Data Group

Upload the *.edr file to the edr.tesla.com web portal for translation into a report (for North American configured vehicles)

Connect to the Tesla vehicle or direct to the module and save a *.edr file to your computer

Maintain the *.edr file on your computer for later report translation in a later version of the EDR service software

Figure 2: Process for obtaining a Tesla EDR report

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Unlike the Bosch CDR Tool software, which translates the data and creates the report on your computer, the Telsa *.edr file is uploaded to Tesla via the Online EDR Reporting Service Portal (https://edr.tesla.com/) and is automatically processed with the most recent translation. An EDR report is generated in an Adobe (pdf ) file to be downloaded to your computer. This process typically takes about half a minute, depending on your connection speed. Like the *.CDRx file in the Bosch CDR Tool, the original Tesla *.edr file should be saved so that it can be retranslated to verify that the translation has not changed prior to testimony, etc.

file from a vehicle intended for distribution outside North America generates the message shown in Figure 3.This does not violate 49CFR563 since it does not apply outside the U.S. and nowhere else in the world is there another regulation requiring the same data access as that found in 49CFR563. The ability of the EDR tool to obtain a *.EDR file from Tesla vehicles remains unaffected. By contacting Tesla Legal staff, law enforcement with an appropriate court order can still obtain an EDR report from *.EDR files collected from non-North American sales vehicles.

When initially released there was some concern expressed that the data sent to Tesla could be altered to create translations that were skewed in favor of the manufacturer. However, it is important to recognize that data translation is automated so that there is not any human involvement before the file is delivered via a secure download from the Tesla EDR Reporting Service Portal.

Retrieval guides for Tesla Models S, 3 and X are available through the Crash Data Group website at https://www. crashdatagroup.com/tesla-edr-kit/. Each retrieval guide is updated periodically and the version or release number or can be found at the bottom of the first page as show in Figure 4.

Tesla EDR Data Retrieval

Worldwide Tesla EDR Access When the Tesla EDR Tool was rolled out in March of 2018, EDR reports could be obtained by uploading *.EDR files from Tesla vehicles worldwide to the Tesla EDR Reporting Service Portal. For vehicles configured for sale in North America, every data element recorded in the EDR report had been verified as correctly recorded and translated. This was not true for vehicles with other configurations, such as right-hand drive vehicles for the UK, or those configured for sale in china.

During this same time frame the General Data Protection Regulation (GDPR) was passed into legislation in the European Union in an effort by the European Commission and the EU to ensure that citizens’ personal data is handled in the appropriate manner by organizations who hold their data. Because crash data is associated with a specific VIN, it was considered by some to be personal data.

Because Tesla EDR reported data had not been verified for vehicles configured for sale outside of North America,

by August 2018 Tesla updated their Online EDR Reporting Service Portal so that only *.EDR files obtained from vehicles intended for distribution in North America can be translated into an EDR report. Uploading a *.EDR

Figure 4: Example of the version or release number of the EDR Retrieval Guide In-Vehicle Connection Analogous to a DLC/OBD II connection, the CAN bus access point can be used to establish an in-vehicle connection in order to perform a crash data retrieval via one of the vehicle’s three CAN bus networks without physically accessing the RCM. As shown in Figure 5, the Model S and Model X, the CAN bus receptacles are located behind the storage pocket under the touch screen and the Model 3’s CAN bus receptacle is located beneath the passenger door sill cover strip by the base of the B-pillar. For the Model 3, the receptacle must be released from the wiring harness before the in-vehicle cable can be plugged in, and then replaced before the vehicle can be driven again.

Once the vehicle communication harness is located, the *.edr data is retrieved by attaching the appropriate cable to

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Figure 5: In vehicle connector locations for Tesla Models S and X (left) and Tesla Model 3 (right) the PCAN adapter and retrieving data through the Tesla EDR software.

Direct to Module Connection The ACM or RCM is located inside the center console under the center stack for Tesla Models S and X, and inside the center console between the front seats for Tesla Model 3. As with most automobiles, a direct-to-module connection requires more disassembly and requires the appropriate direct-to-module cable to be used with the PCAN to establish communication with the RCM to perform a crash data retrieval.

Tesla Diagnostic Logs In addition to crash related data, starting with the Model S, Tesla collects vehicle parameter data from the vehicles’ CAN buses in order to monitor vehicle systems to improve functionality, quality and reliability. The information related to vehicle operation and performance is recorded at 1Hz and include parameters such as: date and time stamps, VIN, alerts, full charge complete, brake on/ off, gear selection, motor RPM, accelerator pedal position, stability control status, driver and passenger seat belt status, crash severity and direction, and warning indicators.

These diagnostic logs are uploaded to the cloud via cellular connection in data packets when the vehicle is not in use.

Tesla makes this information available to law enforcement upon appropriate authority, i.e. search warrant.

Tesla EDR Tool Report and Case Study #1 The Tesla EDR report follows the general layout logic found in most EDR translations from most OE and/or the Bosch CDR Tool. As shown in Figure 6 the report has a “File Information” table and takes advantage of the Adobe Acrobat “book marks” functionality. There is also data limitations, the required Table I and many of the Table II elements laid out in 49CFR563.

The File Information table identifies the version of the software used to retrieve data and used to generate the report (Figure 6). The data limitations are specific to the Model S, 3 and X, and they are similar to that obtained from newer vehicles although perhaps with additional detail including information regarding the data element ranges and resolution. The data tables are comparable to and ordered much the same way as other commonly encountered EDR reports, and the data element names are consistent with those widely used in EDR reports and/or specified in 49CFR563.

Figure 6: Tesla EDR Tool Report, Example File Information table www.collisionpublishing.com

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Figure 7: 2013 Model S – Case Study #1 In the first case study, data was retrieved direct-to-module from a 2013 Model S involved in a crash where it sustained damage to the right front and the left rear side (Figure 7).

There were two events recorded that were 2.6 seconds apart (Figure 8).

In Event 1, a -21 km/h ΔVx and a +6 km/h ΔVy was recorded and all reported devices were deployed (Figure 9).

In Event 2, a -5 km/h ΔVx and -2 km/h ΔVy was recorded and there were not any deployments commanded.

The pre-crash data for the 1st and 2nd events further demonstrate the impact order and fit with the reported 2.6 seconds between events (Figure 10).

As demonstrated above, the data retrieved from a Tesla is comparable to that seen in other 49CFR563 compliant vehicles. The data is laid out in an easy to read manner with data limitations and data tables that are similar to that seen in other EDR reports. In addition, EDR data concepts such as data timing and overlapping pre-crash are similar to that seen in other EDR data sets.

Figure 8: File Information table for Case Study #1

Figure 9: Event 1 Data Record and Deployment Summary for Case Study #1

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Figure 10: Pre-crash data for Event 1 and Event 2 for Case Study #1 Case Study #2 –speed from video and Tesla Diagnostic Logs and EDR in accident reconstruction

moved from the motorcycle as it departed the crash and remained wedged under the front of the Tesla.

In August 2017 a 2014 Tesla Model S rear-ended a 2016 Zero SR motorcycle stopped for a red traffic light in Scottsdale, Arizona. The scene was long, spanning 357 feet.

The motorcyclist was projected a total of 262 feet from impact to rest and sustained fatal injuries. The motorcycle itself was projected 226 feet from impact to rest, 121 feet of which was spent tumbling, before becoming entangled in a barb wire fence on the west side of the roadway. The overall size of the scene gives an impression that the Tesla was traveling at a very high speed, substantially above the 45 mph speed limit, at the time of the collision.

There was no roadway evidence of post-impact braking for the Tesla and the motorcycle was entangled in a barb wire fence before coming to rest. The uncertainty of ascertaining the vehicles’ post-impact speeds limits the usefulness of conventional collision reconstruction methods, such as conservation of momentum.

The Tesla Model S had significant damage to the front end, focused primarily to the right side of the vehicle (Figure 11). None of the wheels were locked due to damage. Based on damage to the windshield it is evident the motorcycle rider left the motorcycle and rode up onto the windshield and roof of the Tesla where he then departed the vehicle. The motorcycle rear wheel assembly was re-

Searle offers a method to bracket the minimum and maximum speed of the rider based on the rider’s post impact trajectory. As shown in Figure 12, using a 0.66 g deceleration for an asphalt surface, the minimum Searle speed is calculated to be 60 mph. A similar calculation for the maximum Searle speed yields 78 mph. With an 18 mph range, as illustrated in Figure 13, there is potential for a significant over-estimate or under-estimate, depending on what speed within the range is considered.

In addition, it is important to recognize that Searle’s speed represents the speed of the rider exiting the collision, and not necessarily the striking vehicle’s impact speed. Particu-

Figure 11: The 2014 Model S and an exemplar 2016 Zero SR

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larly for a roof vault where the vehicle passes beneath the rider, the vehicle’s impact speed is faster than the rider’s postcollision speed. Under these circumstances, the Searle min and max speed range would be biased towards lower speeds, which would give the benefit of the doubt to the suspect.

The collision in this example pre-dated the Tesla EDR Tool.

The only known available electronic evidence was the Diagnostic Logs. A search warrant was delivered electronically to Tesla for typical 49CFR563 Table One information: Vehicle Speed, Brake Switch position, Accelerator Pedal position, steering wheel angle, and (not 563 related) forward looking brake assist over a time period between 1148 until 1200 hours on the date of the collision. Tesla satisfied the request within a week by providing an Adobe (pdf ) file with the requested information spanning the time period requested.

Figure 12: Searle Minimum Speed Calculations

As seen in Figure 14 the information organized in a spreadsheet form with data titles along the top and VIN and UTC time columns to the left. From the data, the apparent resolutions are 0.1 mph for speed, 0.2% for accelerator pedal position, 0.1 degrees for steering wheel angle. Brake

pedal is reported as on/off and steering polarity follows the right hand rule and SAE J211 with a right steer as positive.

Values of 179.8 mph and +/- 819.1 degrees are recorded when the vehicle is off.

Other than the column headers, Tesla offers no data limitations or further explanation of the diagnostic log data.

As seen in Figure 14, the VIN identifies the vehicle involved in the crash, and the time of the crash is indicated by the row containing the word “CRASH” in the frontal crash detection column. It is noteworthy that data is generally recorded at even seconds, but a row with a fractional second time step is created for the beginning of brake applications, and crash detected. After crash detection, data continues to be recorded in fractional second increments for several samples as also shown in Figure 14. Using the fact that the crash occurred in the one second time interval with the 11:52:16 time stamp yields another impact speed range of 75 to 84 mph, as illustrated graphically n Figure 15.

In response to the search warrant, Tesla also indicated, “You may find it relevant to refer to the attached page from the Tesla Model S Owner’s Manual which explains, ‘Automatic Emergency Braking operates only when driving between 5 mph (8kph) and 85 mph (140 kph).’” Since the recorded speeds did not drop down into the range until after the collision, this fatal crash was not the result of an AEB system failure.

Figure 13: Impact speed range using Searle

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Figure 15: Impact speed range from Tesla Diagnostic Logs A search of NHTSA’s vehicle recall database indicated that there were 3 recalls for the 2014 Model S, but searching by the VIN confirmed that only 1 recall applied to the subject vehicle. That recall was related to a loss of power steering assist due to corrosion. Clearly, corrosion is not a problem normally encountered in the Arizona desert and increased steering effort was not described, nor would it be a cause for this fatal crash. Therefore, this recall is not relevant.

According to news stories, other Tesla crashes have been attributed to problems with Tesla’s Autopilot. All Tesla vehicles manufactured since October 2014 are equipped with Autopilot 1. However, a firmware update removed some of the self-driving features. All Tesla vehicles manufactured after October 2016 are equipped with Autopilot 2, which is capable of full self-driving. The speed limit

for Autopilot was raised from 80 to 90 mph in a 2017 update. The subject vehicle was not being driven in Autopilot mode, since it did not have Autopilot 2, having been manufactured in December 2014 as indicated on its VIN plate, and its speed was above the Autopilot speed limit.

In addition, because the vehicle will slow via regenerative braking when the accelerator pedal is released, the precrash speeds, which do not exhibit a steady speed with no accelerator pedal application also confirms that the Tesla was not being driven in Autopilot mode. In fact, as shown in Figure 14 and Figure 18, the pre-crash data shows the typical pattern of accelerator pedal release followed by brake application a short time later. As such, it can only be concluded that the driver, and not the Autopilot, was in control of the Tesla and responsible for this fatal crash.

Tesla EDR Data When the Tesla EDR tool was released, the suspect vehicle had been stored for seven months at a secure police impound lot, at the Scottsdale Police Department’s property and evidence facility. A second search warrant was written and granted on the suspect vehicle and on June 4, 2018 the data was imaged using a direct-to-module cable.

Figure 16: EDR Report File Information for case study #2

Figure 17: Event Data for Case Study #2 www.collisionpublishing.com

The file information table shown in Figure 16 includes identification, event and user entered information, such as user comments.

The VIN is read from the module. Space for the name of the requestor was available in this first version of the Tesla EDR Service, but was not included in the later version, as demonstrated in the Case Study #1 File Information shown in Figure 8.

Figure 17 shows that the Tesla EDR reported maximum longitudinal ΔV was -17 kph (-10.5 mph) at 153 msec and the maximum lateral ΔV was -2 kph (-1.2 mph) at 33 msec.

A time to maximum resultant ΔV is reported, although the maximum resultant ΔV is not provided. The ignition cycles at event, 6288, is one less than the ignition cycles at retrieval shown in Figure 16. The Airbag MIL was off and both front seat belts were unbuckled, and the front passenger seat occupant classification was “empty.”

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Figure 18, Precrash Data for Case Study #2 The deployment summary indicates which safety equipment was commanded to deploy and deployment times.

Comparison with the deployed safety equipment observed within the vehicle further supported the premise that this this EDR file was a record from this fatal crash.

The data from case study #2 is shown in Figure 18. From the pre-crash data, it is evident that vehicle speed is reported in 1 kph resolution, which according to data limitations is rounded to the nearest kph. The accelerator pedal position has 1% resolution, the motor RPM has 100 RPM resolution, and the steering wheel angle’s resolution is 4.2 degrees.

The suspect vehicle speed at T = 0 was reported to have been 132 kph, or 82 mph. The highest reported speed was 169 kph, or 105 mph before braking at T = -2 secs and the ABS became active at T = -0.5 secs.

Accounting for allowable speedometer error, the underreporting of speed due to ABS slowing wheel speeds, and the maximum amount of slowing due to braking after the last reported sample before impact results in a range of 74 mph to 89 mph as demonstrated in Figure 19 and shown graphically in Figure 20. This latter assumes that the last reported speed could have been as old as half a second, as has been observed in older designs from other manufacturers in order to give maximum benefit of the doubt to the suspect.

Utilizing ΔV to arrive at an impact speed is a common and acceptable means of analysis. When using this approach, one must consider a number of factors, including the following:

1. Was the entire crash recorded?

2. Did the recorder miss any ΔV prior to wake up?

3. Was there any data clipping?

4. Does an adjustment for offset need to be made, (effective mass ratio)?

5. Did the recorder capture too much, (before or after the crash)?

6. Are ground forces effecting the calculation?

Fortunately, the Tesla EDR report contains information that is useful for this type of analysis. In addition to the ΔV graph, the Tesla EDR report provides a graph of the vehicle’s impact acceleration, as shown in Figure 21. This is useful for determining when the crash pulse ends in order to evaluate if the entire crash pulse was recorded.

Figure 20: Impact speed range from pre-crash speeds 100 Collision Magazine - Volume 13 Issue 2

Figure 19: Impact Speed Calculations

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Figure 21: Telsa EDR vehicle acceleration

Figure 22: Impact eccentricity

Any additional ΔV that was measured after the crash pulse ended can be removed after the crash pulse end evident in the acceleration graph. As with any threshold activated recorder, it is possible to miss a small amount of ΔV due to the sub-threshold acceleration preceding module wake-up, but this is generally small, or even negligible for impacts outside the realm of low speed. The presence or absence of the clipping flag indicates if data clipping occurred.

ground forces results in a lower impact speed calculation, which again lends benefit of the doubt to the suspect.

Adjustment can be made for the impact’s eccentricity or offset between the line of action of the collision force and the vehicle’s airbag control module/center of mass. Figure 22 shows that the area of the centroid of damage was approximately 18 inches to the right the vehicle’s centerline.

As shown in the pre-crash data, the driver was braking sufficiently hard to engage the ABS during the last 0.5 to 1.0 seconds. Because a frontal impact would only cause the driver to apply the brakes harder, it is fair to assume that the vehicle brakes would be active during the crash. Because the accelerometer within the vehicle’s airbag control module cannot distinguish between deceleration from the crash, and that from continued braking, the measured ΔV is the combination of ΔV due to impact and slowing due to braking. Removing the possible additional ΔV due to

The impact’s restitution affects the closing speed of impact.

This parameter must be estimated since it cannot be measured. The restitution of normal large collisions, including the 35 mph barrier crashes for the New Car Assessment Program, generally run about 0.1 or 10%. As a collision becomes more inelastic, the vehicles’ tendency to stick together increases and the restitution approaches zero. As shown in Figure 11, the motorcycle’s rear wheel assembly was detached and remained with the Tesla, indicating a lower restitution. Based upon this information, a lower restitution of 5% was chosen to be used with effective mass ratio in the closing speed calculations, shown in Figure 24.

The motorcycle was stopped according to witnesses, as well as what was shown on a surveillance video. Therefore, the closing speed will also be the Tesla’s impact speed, which was calculated to be 81 mph. Accounting for the potential

Figure 23: Calculation of automobile and motorcycle ΔV’s

Figure 24: Closing speed calulation

Once the effect of ground forces on the automobile’s ΔV has been properly accounted for, the V of the motorcycle can be calculated by applying the principal that the ΔV’s are inversely proportional to the ratio of vehicle masses as shown in Figure 23.

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Figure 25: Impact speed range from EDR reported Delta V for error allowed in Part 563, or +/- 10%, results in an impact speed range of 72 to 90 mph and depicted graphically in Figure 25.

Video Evidence Every investigation includes a search of the area for potential surveillance camera footage. This led to a video recording of the event obtained from a dealership bordering the roadway where the collision occurred. Figure 26 shows video frames depicting the appearance of the motorcycle and the Tesla. The video shows the motorcycle come to a stop at the intersection and then moments later, the red Tesla approached the intersection at high speed and collided with the motorcycle and rider.

Roadway evidence was used to determine the point of impact and a frame by frame review of the video was utilized to determine a specific location for the Tesla from which a distance to the area of impact could be used for a time distance speed calculation. This was accomplished by placing a patrol vehicle in a location comparable to that of the Tesla captured in a frame of the surveillance camera video.

Figure 27 shows two video frames, representing the patrol vehicle at the same location as the Tesla.

The distance measured from the patrol vehicle to the area of impact was 357 feet. The frame rate of the raw video data was determined to be 5 frames per second. The video evidence was then again reviewed frame by frame to determine the frame nearest the collision. This was determined to be frame 813 and can be seen in Figure 28. Once this

was accomplished, Input Ace, Video specific software, was utilized to verify the exact time between frames as well as the time from frame 800 to frame 813. This was determined to be 2.6 seconds. This allowed a simple time distance calculation. Accounting for possible errors in time associated with one frame, and +/- 5 feet for impact location results in an average speed within the range of 86 to 103 mph over the 2.4 to 2.8 seconds preceding the collision.

The Diagnostic Logs, EDR pre-crash speed and EDR ΔV, all indicated an impact speed for the Tesla within the approximate 75 to 84 mph range, and the Diagnostic Logs and EDR pre-crash data recorded top speeds within the 105 to 107 mph range. The impact speed compared favorably with the more conservative pedestrian throw analysis and the top speed compared favorably with the video analysis. As demonstrated in this case study, the electronic data from the Tesla, greatly enhanced the speed analysis of this fatal automobile to motorcycle collision.

Summary and Conclusions As outlined through two case studies, the Tesla EDR report is detailed and organized in a familiar manner. Like other EDR information, the Tesla EDR data is a valuable addition to a situationally complete reconstruction.

The Tesla Diagnostic Logs provide additional information which may be slightly different, due to parameters being sampled at different times and consistent with its different intended purpose.

Figure 26: Surveillance video frames showing the appareance of the motorcycle and Tesla 102 Collision Magazine - Volume 13 Issue 2

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Figure 27: Surveilance video frames shown the Telsa and patrol vehicle at a comparable location

Figure 28: Collision depicted in surveillance video frames In the second case study, impact speeds obtained from data from the EDR and Diagnostic Logs, traditional momentum analysis, as well as video analysis demonstrated a consistent speed range, which was substantially above the speed limit.

for example, both electronic data, as well as video analysis, confirm that pre-crash the suspect was driving the Tesla at about 100 mph, which is nearly twice the speed limit.

In addition to impact speed in the second case study, by confirming the Diagnostic Log data was related to this fatal crash through date and time, and the EDR Report data was related through ignition cycles and reported parameters that matched seat belt use and deployed devices www.collisionpublishing.com

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Tesla EDR Tool Kit

EDR Data can be retrieved from all Tesla Model S, Model X, Model 3, and Model Y vehicles. Tesla Roadster, which was produced from 2008 to 2012, is equipped with a RCM but does not have an EDR.

The EDR Retrieval Hardware Kit for Tesla vehicles contains all the hardware required to download the Event Data Recorder (EDR) data that may be stored in all Tesla Model 3, Model S, Model X, and Model Y vehicles. This is the only kit that contains genuine Tesla EDR cables.

Available at Crash Data Group www.crashdatagroup.com (800) 280-7940 crash@crashdatagroup.com

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Identifying Infotainment Systems for Use in Accident Reconstruction Shawn Harrington Forensic Rock

I

ntroduction Like its big sibling Event Data Recorder nearly two decades ago, infotainment and telematics systems have burst onto the accident reconstruction scene in the past decade. Similar to early EDR coverage and the amount of data recovered, supported vehicles containing useful infotainment data for the accident reconstruction community is relatively small but growing. The application of data imaged from infotainment systems has been extremely helpful in criminal prosecutions involving stolen vehicles, human trafficking, homicides, drug trafficking and more. Berla, the manufacturer of hardware and software to image data contained within infotainment and telematics systems, has done an excellent job increasing vehicle coverage and support in the last five years. The intent of this article, however, is to focus solely on the practical applications of infotainment data relevant to the accident reconstruction community.

In 2015, Lemere authored an overview of infotainment data relating to the field of accident reconstruction and presented a hit-and-run case study.1 In a 2017 SAE technical paper, Bortles et al. comprehensively introduced the accident reconstruction community to the Berla iVe infotainment ecosystem, conducting testing of various infotainment systems and presenting the results.2 In 2018, Vandiver and Anderson presented validation testing of Ford Sync Gen 2 and Sync Gen 3 infotainment systems, concluding that the speed data reported in these respective systems was acceptably accurate.3 A comprehensive search of the technical literature, however, failed to reveal any articles evaluating infotainment data and their involvement in actual collision events.

This two-part article hopes to present a modest beginning to this area in accident reconstruction. In this first section, systems that contain relevant data to the acci-

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dent reconstruction community will be discussed. In the second part, correlation of EDR and infotainment data will be presented.

Hundreds of vehicles involved in collisions which contained infotainment systems and their imaged data have been reviewed and analyzed by the author. Figure 1 demonstrates all vehicle makes that contain supported infotainment systems covered by Berla’s iVe software. While the list of supported vehicles containing infotainment data is laudably immense when conducting a search on Berla’s website or iVe software, personal experience has concluded that only a subset of these infotainment-supported vehicles have potential data relevant to accident reconstruction analyses.

It is important to note that infotainment data is trimlevel dependent. That is, only certain trim levels within a given model line are supported by a Berla infotainment imaging. For example, while every 2015 Dodge Charger will have a supported Bosch Crash Data Retrieval (CDR) system, only certain trim levels will have a Berla-supported infotainment system. Further complicating system identification is the fact that for some infotainment acquisitions, only certain manufacturers of the infotainment module itself are supported. That is, while a certain trim-level of a model of vehicle may be supported, only the specific Harman-manufactured infotainment system module is ultimately supported by a Berla iVe infotainment acquisition. This will be discussed further in the vehicle identification section.

Based on the author’s experience, Berla’s vehicle lookup section is ultimately beneficial for determining whether a specific vehicle has the potential of being supported.

Berla continues to improve their infotainment system identification by providing useful photographs and identification tips in their ‘Need Help Identifying’ sec-

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Figure 1: All Berla-supported infotainment systems.

Figure 2: Berla systems that can provide useful data for accident reconstruction.

tion. However, due to the immense volume of different vehicle makes, models, and trim levels their software covers, it is not yet an exhaustive resource. Acquiring resolute photographs of the center stack area of the vehicle before committing to a vehicle inspection for an infotainment imaging is a recommended practice. Berla is an impressively willing and valuable resource for fieldand photo-identification of infotainment systems.

Once you have identified the infotainment system in the vehicle of interest, Berla provides you with the data that is supported by an imaging of that infotainment system.

This checklist should only serve to alert you to the potential of acquiring certain data, not specifically that this information will be provided in your imaging. For example, tracklogs from a Uconnect infotainment system have never been discovered in the field by the author or his colleagues, yet, tracklogs are always checked when

screening an infotainment system on Berla’s website for a Uconnect system.

When attempting to parse out which information is valuable to the accident reconstruction community, priority to information concerning vehicle speed, braking, location, and other collision-related information were given. While cell phone data such as whose phone was paired to the vehicle is incredibly valuable for other types of investigations, specifically stolen-vehicle scenarios, this information is not as important to a classic accident reconstruction analysis. For these reasons, based on the author’s experience dealing with infotainment systems, the manufacturer’s in Figure 2 provide the reconstructionist a realistic chance at recovering useful collisionrelated information. The quality and quantity of this collision-related information to an accident reconstruction analysis, however, varies greatly. Three tiers of data are therefore presented.

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Model Year Range: 2016 – Newer Useful Infotainment System: Sync Gen 3

signatures which can be very useful. Traction and hard acceleration/deceleration events are often useful in determining the precise second the collision occurred. Correlating SG3 infotainment data with EDR data will be the topic of the second part of this article.

The Ford Sync Gen 3 (SG3) system is the premium trim level infotainment system found in 2016 and newer top trim level Fords and most Lincoln vehicles. Identification of this system is easy if you have an image of the center stack of the vehicle. In all other 2016 and newer Ford vehicles where a SG3 system is not installed, the Sync Gen 1 (SG1) system is typically contained in the vehicle (all Lincoln’s typically have the SG3 system). The SG1 system has never contained useful information for accident reconstruction purposes and can be safely ignored by the collision reconstructionist.

If the vehicle is totaled or not being driven after a collision, perhaps the best part of SG3 data is the likelihood that you will have data related to your collision event with the SG3 system. It must be noted that every time a door opens, the SG3 system writes data that pushes (typically) older data due to storage constraints in a first-in, first-out algorithm.

Reconstructionists must be cautious when handling these systems to physically remove the infotainment modules first from the vehicle at an inspection and implement a power-neutral protocol to avoid the loss of potential data relating to the event of interest.

Figure 3 demonstrates two identical Ford Focus sedans containing two different Ford infotainment systems, the SG1 and SG3. This highlights the importance of getting a photograph of the center stack area of the vehicle to determine which infotainment system the vehicle contains.

Based on the number of systems this author has imaged, however, the confidence level of finding useful data in a totaled or grounded (non-driven) vehicle related to a collision event is high. If the vehicle is driven after the collision event of interest, an investigator typically has only 6 – 10 days (utilizing average driving) by the vehicle before the collision event is overwritten, so time is of the essence.

The Top Tier: The Gold Standard Ford/Lincoln

The data in the SG3 system has excellent uses for accident reconstruction purposes. The SG3 system gives the reconstructionist two different sources of vehicle speed data, both a 1-Hz GPS based vehicle speed system and a variable (typically 9-10 hZ) vehicle speed system reported from the CAN bus. These two independent sources of speed information are known as tracklogs and velocity logs, respectively. In addition, time, date, and location information is written to this speed information, which gives the reconstructionist access to GPS-based time and date

Remember, the main benefit of infotainment data compared to EDR data is that there is no trigger event to record infotainment data. Therefore, the possibility exists of imaging SG3 infotainment data from events that do not typically trigger non-deployment events in the EDR such as pedestrian/bicycle collisions, low speed impacts, and sideswipe events. One of the major benefits of this data is that it is not only time and date stamped, but geo-stamped as well.

Figure 3: SGI (left), identical vehicles (center), and SG3 system (right)

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The Middle Tier: The Useful, But Inconsistent Players Ford/Lincoln Model Year Range: 2011 – 2016 Useful Infotainment System: Sync Gen 2 The reason the Ford/Lincoln Sync Gen 2 (SG2) is not elevated to the top tier of infotainment systems for accident reconstruction is the reliability of acquiring collisionrelated data. Whereas every single SG3 system regardless of whether it is equipped with a navigation system records navigation-based tracklogs, the SG2 system must be equipped with a navigation system to report useful collision-related data. This is often difficult or impossible to determine from information such as the VIN or a photograph of the center stack.

If the SG2 system is equipped with navigation, the other challenge related to SG2 data is the algorithm that writes data to the memory. As reported by Vandiver and Anderson, SG2 infotainment systems use a wear level algorithm to write data to the memory. [3] This random writing to memory in the SG2 system has the unfortunate effect of potentially overwriting the collision event at issue. Whereas nearly every SG3 system the author has investigated related to a collision event has captured the collision, only a fraction of SG2 systems investigated have captured the collision of interest. A formal percentage of success with SG2 systems will be reported in part two of this article.

The useful data related to collision events is the one (1) Hz GPS-based vehicle speed. This data has the benefit of being time, date, and location stamped. Events such as gear shifts, status of parking lights, and door opening events can be useful to collision investigations. As reported by

Bortles et al., however, the absence of a door or parking light event does not conclusively prove that the event did not occur. Rather, Bortles et al. concluded that the presence of a vehicle event such as a gear shift or a parking light event is evidence that it did occur at that time, date, and place. [2] This is consistent with testing conducted by this author.

Given the wear level algorithms and the random nature of the data written to memory, inconsistent tracklogs are often observed in SG2 systems. The random nature of the data is why this system is in the middle of the infotainment pack.

Nissan/Infiniti Model Year Range: 2008 – 2019 Useful Infotainment System: 08IT 8000 Series There are two supported infotainment systems manufactured by Nissan/Infiniti and supported by Berla, the 08IT 3000 series and 08IT 8000 series. For purposes of accident reconstruction, the 3000 series provides no useful collision related data.

The most challenging aspect of Nissan/Infiniti systems is identifying whether the system is supported by a Berla acquisition. There is no exterior identifier when looking at a Nissan/Infiniti to determine whether it is a 3000- or 8000-series system. One way to identify whether a Nissan/ Infiniti has a Berla-supported 3000- or 8000-series system is to look for the presence of a ‘MAP’ button. If the system does not have a ‘MAP’ button, it does not have a 3000nor an 8000-series infotainment system and is definitely unsupported. (Figure 4)

Figure 4: Examples of supported 3000/8000 series Nissan/Infiniti infotainment system (left) and unsupported 3000/8000 series system (right).

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If a Nissan/Infiniti is in fact supported, whether it is a 3000or 8000-series cannot be determined without a physical inspection of the vehicle. Identification of the 3000-series is made by looking at the model number on the top of the module after it has been removed. If the model number starts with “NAU-3”, the system is a 3000-series. The 3000-series does not contain useful collision-related data.

This information is never meant to preclude an imaging of the system, as imaging electronic data is always recommended for completeness. No useful collision-related data, however, has ever been found in a 3000-series Nissan/Infiniti system. (Figure 5)

collision event. One benefit of this system is that EDR coverage for Nissan’s did not begin until 2012, yet, infotainment systems with speed data are supported beginning in 2008. The author has imaged a 2010 Nissan Maxima infotainment system that reported GPS speeds once per second that were useful in reconstructing the collision.

This vehicle was the bullet vehicle in a collinear rear impact with a stopped target vehicle. The reported GPS speed from the infotainment system matched the required speed of the bullet Nissan to match the damage-based delta-V’s to the respective vehicles. (Figure 7)

The procedure to identify the system useful to accident reconstruction, the Nissan/Infiniti 08IT 8000-series, is made in a similar way to the 3000-series.

The model number on the top of the module will start with a “NAU-P8” which identifies the 08IT 8000-series. (Figure 6) Similar to the Ford SG2 system, the Nissan/Infiniti 8000-series infotainment systems reports one (1) Hz GPS-based vehicle speed data that is date, time, and location stamped. Useful one-Hz pre-collision speed data can be found for the entirety of the trip prior to the

Figure 5: Example of a Nissan/ Infiniti 3000-series.

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Figure 7. One (1) Hz GPS speed data from a 2010 Nissan Maxima Infotainment System.

Figure 6: Berla-supported Nissan/Infiniti 08IT 8000-series infotainment system useful for collision reconstruction purposes.

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Not as Useful (and Potentially More Expensive) – The Bottom Tier Mercedes-Benz Model Year Range: 2011 – 2018 Useful Infotainment System: NTG 5*2 There are two different systems contained within the Mercedes-Benz family supported by Berla, the NTG 4.5 and NTG 5*2. For accident reconstruction purposes, the NTG 4.5 infotainment system can be ignored as it has failed to provide relevant data useful in collision investigations. The NTG 5*2, however, offers potential for a wealth of data, not all of which will be useful for accident reconstruction purposes Identification of the NTG 5*2 system has proven to be difficult. This system highlights one of the challenging aspects of infotainment system identification, specifically that only certain Harman-manufactured units are Berlasupported. For example, NTG 5*2 systems manufactured

by Panasonic and Mitsubishi are unsupported by a Berla acquisition. Often, this can only be identified once the infotainment module is physically removed from the vehicle.

(Figure 8) One identifier, however, has proven reliable in the field for identifying supported NTG 5*2 Mercedes-Benz systems. Supported NTG 5*2 Mercedes-Benz infotainment systems do not have a bezel around the perimeter of the screen, whereas unsupported systems have a plastic bezel.

If you only have photographs of the center stack area and are weighing the potential of acquiring infotainment data, this can be a good filter to determine your chances of success. (Figure 9)

Figure 8: Identification of supported Mercedes-Benz NTG 5*2 manufactured by Harman.

Tracklogs are the data of interest for the collision reconstructionist. The tracklogs that are stored in the MercedesBenz 5*2 infotainment system are GPS-based tracklogs with a variable frequency. Unlike the middle tier of vehicles, the reported speed data in Mercedes-Benz NTG 5*2 systems follow no consistent guidelines for the frequency of data the infotainment system records. While it typically will record at least one data point over the course of one minute of vehicle driving, the frequency of data points per minute can be anywhere between one and ten samples.

Given this inconsistency, the data may be useful for collision investigations, but it is certainly not as reliable or helpful as the middle tier of vehicles. The speed information from NGT 5*2 systems also contains date, time and location information reported with the data.

While the quantity of tracklogs stored in the NTG 5*2 systems has been inconsistent, the potentially comprehensive range of the recorded tracklogs is one area where the NTG 5*2 system can stand apart from the others. The storage of tracklogs in the Mercedes-Benz NTG 5*2 system varies from every single tracklog the vehicle has ever generated (i.e. you can see every trip the vehicle has made) to a firstin, first-out 6 – 10 driving-day storage system similar to the Ford/Lincoln SG3. The author has found no reason as to why some Mercedes-Benz systems store so much data, and some store relatively little. The chance exists, however, of capturing data from the subject collision event in an imaging years after the collision event occurred. This alone is a unique and useful feature of the Mercedes-Benz NTG 5*2 infotainment system.

Figure 9: Supported MB NTG 5*2 system with no bezel (left), unsupported MB NTG 5*2 system with a plastic bezel (right).

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If a vehicle owner is paying for a Hyundai, Kia, or GM telematics subscription service, this would be a definite reason to image these respective infotainment systems.

FCA Model Year Range: 2012 – 2017 Useful Infotainment System: Uconnect 8.4 / 8.4N There are two limitations with FCA Uconnect data. One, the acquisition typically requires a destructive chip-off methodology which destroys the original unit and prevents its reinstallation into the vehicle. Therefore, a new unit must be purchased if an imaging is being conducted on a vehicle that has been repaired or is going to be repaired. Second, there is no speed information nor location information reported in the data. Therefore, the uses of the data are limited.

The benefit of Uconnect data to the reconstructionist is the presence of time and date stamped odometer readings. Using this data, average speeds can be calculated, and vehicle timelines can be established. Care must be taken as these readings can be inconsistently reported which is unfortunately a limitation of the data. (Figure 10) Although limited, this information can be useful in collision investigations. Furthermore, matching odometer readings reported in FCA EDR Data have been utilized to establish the precise time and date of the collision event.

This has been accomplished by utilizing the date- and time-stamped odometer information from the infotainment data.

Final Impressions

Newer BMW Berla-supported infotainment systems have reported tracklog information. This information, however, is on the order of one data point of GPS-based vehicle speed every 15 – 20 seconds and is therefore not as useful to the accident reconstruction community.

More work needs to be completed on newer Honda and Acura systems. It has been reported to the author that the Acura Berla-supported infotainment systems would fall into the middle tier in terms of reporting one (1) Hz GPSbased speed data.

As always, instructions for the removal of any of the infotainment systems discussed in this article can be found using Berla’s software. Using a resource such as ALLDATA can be a valuable shortcut to finding the location of infotainment systems in vehciles. Many hours of frustrating in-vehicle searches for infotainment systems could have been saved by first locating the infotainment module in a resource such as ALLDATA. It must be noted that infotainment systems and the data presented within this article are the author’s best recommendations based on my experience. Unlike the now standardized world of EDR, infotainment data and coverage can literally be all over the map. The findings presented in this article are my best recommendations to streamline your decision making. If you are in doubt, my recommendation will always be to image the available data.

The systems given above can be useful sources of vehicle data when reconstructing a collision event. It must be noted that systems that were not reported above do not preclude the chance of recovering useful collision-related information. For example, one issue with Hyundai, Kia, and GM On-Star equipped vehicles are that they generally require subscription-based services to be active to provide the chance of useful vehicle information related to collision events. These subscriptions often lapse after the first free year by vehicle owners. Given that GM OnStar Gen 10 systems, the On-Star unit that is currently being installed in newer GM vehicles, is unsupported by a Berla infotainment acquisition, finding collision-related information in telematics subscription-based services has not proven to be a consistent Figure 10: Timeline established from odometer and successful methodology thus far. readings from FCA Uconnect infotainment data.

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In part two of this series, a deep dive into the correlation between infotainment data and EDR data will be explored.

A methodology for relating these separate sources of electronic data will be presented.

Shawn Harrington is an accident reconstructionist and forensic engineer with Forensic Rock. He can be reached at sharrington@forensicrock.com.

Acknowledgments: The author would like to thank Scott Kline of ARCCA, Brandyn Devanney, Adam Taylor, Michael Helker, Kevin Miyashiro, Olivia Schuler, Nate Albuck, Greg Berbig and Ken Case of Berla, Bryan Marangoni of GEICO, and Cory Rodriguez from the New Jersey State Police for all their help with the information contained within this article. The author would like to especially thank ARCCA for all of their help and support.

References 1. LeMere, B., “The Rest of the Story,” Collision: The International Compendium for Crash Research, Volume 10, Issue 2., Fall 2015: pages 16-17, Print.

2. Bortles, W., McDonough, S., Smith, C., and Stogsdill, M., “An Introduction to the Forensic Acquisition of Passenger Vehicle Infotainment and Telematics Systems Data,” SAE Technical Paper 2017-01-1437, 2017, doi:10:4271/2017-01-1437.

3. Vandiver, W. and Anderson, R., "Analysis of Berla iVe Acquisitions of Vehicle Speed Data from Ford Sync Systems," SAE Int. J. Trans. Safety 6(3):257-274, 2018, https://doi.org/10.4271/2018-01-1442.

register today March 9-11, 2020 - Houston, Texas www.crashdatagroup.com/edr-summit

Guardrail Crash End Terminal Reconstruction and Analysis Lawrence A. Wilson, P.E. Dr. Kevin Schrum, PhD, P.E. Wilson Consulting, LLC University of Alabama - Birmingham

A

bstract This paper serves as a primer to the reconstruction of motor vehicle crashes involving guardrail end terminals. The purpose and function of various guardrail components are presented and the standards which dictate the design and performance of guardrails are described herein.

The primary functional components of a guardrail system are the guardrail face and the end terminal.1 The guardrail face is the length of guardrail that runs along the road. The end terminal is the upstream end of the guardrail.

This paper will focus on installation issues, maintenance issues, and accident reconstruction techniques related to guardrail end terminals. Energy-absorbing end terminals will be the primary focus of these topics.

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I

ntroduction The primary function of a guardrail is to shield a motorist who has left the roadway.2 Guardrails function as a system. This system includes the rail [or cables], the posts, the soil into which the posts are installed, the connection of the guardrail to the posts, the end terminal, and the anchoring system at the end terminal. All of these elements affect the in-service performance of the guardrail upon impact.3 Guardrail system crashworthiness is assessed through standardized crash tests. From 1981 to 1993, the Federal Highway Administration [FHWA] had required that guardrail systems on the National Highway System [NHS] must comply with testing and performance criteria outlined in National Cooperative Highway Research Program [NCHRP] Report 230.4 In 1993 NCHRP Report 350 was published, and the FHWA began adopting the standards outlined therein. In 1998, the FHWA required full compliance with the recommended guidelines outlined in NCHRP Report 350 for guardrails and guardrail end terminals. In 2009, the Manual for Assessing Safety Hardware (MASH) was published as a replacement to NCHRP 350. After that date, new devices were tested in accordance with the guidelines in MASH, but states could continue installing devices that predated MASH. At each point in this history, the FHWA would issue acceptance or eligibility letters to the owner of a device, certifying that it had passed the required tests.

The FHWA continued to issue these letters for NCHRP Report 350 test results up until 2016, when the FHWA changed its policy regarding the issuance of these letters.5 From that time forward, in order for the FHWA to issue a letter of eligibility, making the device eligible for federal reimbursement, the device had to conform to MASH test standards.

In order to receive federal reimbursement, state and local highway agencies must comply with the current standards adopted by the FHWA. For this reason, standards and specifications of state and local highway agencies generally correlate with the standards adopted by the FHWA [NCHRP Report 230, then NCHRP Report 350 and subsequently MASH]. However, acceptance of a guardrail or guardrail end terminal by FHWA does not ensure acceptance by various state or local highway agencies. Likewise, guardrails and guardrail end terminals

other than those accepted by FHWA could be acceptable for use on the NHS. If a particular device meets the relevant FHWA standards, this device may be used by a state or local highway agencies with concurrence by its FHWA division office, on the NHS. Because of potential variance between states and jurisdictions within states, it is recommended that the relevant state and local highway agency standards and specifications be consulted when analyzing a motor vehicle crash involving a guardrail or guardrail end terminal.

Because NCHRP Report 350 served as the standard for guardrail system testing for the large majority of the last 25 years, this paper will reference these testing standards when discussing testing criteria as it is applied to the reconstruction of motor vehicle crashes involving guardrails. The relevant NCHRP Report 350 crash tests will be outlined in detail. Any relevant differences between the crash test standards between NCHRP Report 350 and the current MASH standard will be noted when necessary.

NCHRP Report 350 - Test Vehicles Production model vehicles are used in NCHRP Report 350 crash tests to evaluate the crashworthiness of guardrails and guardrail end terminals. NCHRP Report 350 utilizes five vehicle classes in these crash tests. These vehicle classes are listed below: •

Small car – 700 kg [1,500 lb]

•

Small car – 820 kg [1,800 lb]

•

Pick-up truck – 2,000 kg [4,400 lb]

•

Single-unit truck – 8,000 kg [18,000 lb]

•

Tractor-trailer – 36,000 kg [80,000 lb]

The crash tests to evaluate typical W-beam guardrails and guardrail end terminals utilize the 1,800 lb. small car and the 4,400 lb. pick-up truck. In crash test reports, these vehicles are generically referred to as the 820C and 2000P vehicles, where the numeral refers to the weight in kilograms and the letter refers to “car” or “pickup”. The detailed vehicle specifications for these vehicle classes are listed in Table 1.

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Property Mass [kg] Test Inertial Dummy Maximum Ballast Gross Static Dimensions [cm] Wheelbase Front Overhang Overall Length Track Width Center of Mass Location [cm] Aft of Front Axle Above Ground Location of Engine Location of Drive Axle Transmission

820 kg Small 2,000 kg PickCar up Truck 820 ± 25 75 80 895 ± 25

2000 ± 45 --200 2000 ± 45

230 ± 10 75 ± 10 370 ± 20 135 ± 10

335 ± 25 80 ± 10 535 ± 25 165 ± 15

80 ± 15 55 ± 5 Front Front

140 ± 15 70 ± 5 Front Rear

Manual or Automatic

Manual or Automatic

Test level 1 utilizes an impact speed of 50 kph [31.0 mph] with a tolerance of ±4 kph [2.5 mph].11 Test levels 4-6 are applicable to only longitudinal barriers, such as bridge rails and concrete barriers. Test level 4 includes the 8,000 kg [17,600 lb.] straight truck, which is referred to as a “single-unit truck” in NCHRP Report 350.

Its vehicle designation in test reports is “8000S”, where the numeral refers to its mass in kilograms, and the “S” stands for Single-Unit Truck. The current MASH standard uses a 10,000 kg [22,046 lb.] straight truck (referred to as “10000S”). Test levels 5 and 6 include the 36,000 kg [80,000 lb.] tractor-trailer. This vehicle may be either a tractor and box trailer (“36000V”) or a tractor and tank trailer (“36000T”). The box trailer is used in test level 5 impacts, whereas the tank trailer, filled with water, is used in test level 6 impacts.

NCHRP Report 350 Crash Criteria for Guardrail End Terminals Test 30 is conducted with the vehicle approaching parallel to the roadway [0-degree impact angle], with the impact to the left or the right of the vehicle as shown in Figure 2.

Table 1: NCHRP Report 350 Recommended Test Vehicle Properties 6 The current MASH standards used to evaluate guardrail and guardrail end terminal crashworthiness use a heavier small car and a heavier pickup truck. The small car weight increased from 820 kg [1,800 lb.] to 1,100 kg [2,420 lb.] and the pick-up weight increased from 2,000 kg [4,400 lb.] to 2,270 kg [5,000 lb.].7 The vehicle designations found in crash test reports also changed to 1100C and 2270P, respectively.

NCHRP Report 350 – Test Levels NCHRP Report 350 outlines six test levels that may be utilized to evaluate roadside safety hardware. Most crashtested safety features, including guardrails and guardrail end terminals have been qualified to test level 3 and are intended for use on a wide range of high-speed arterial highways.8 Test level 3 involves an impact speed of 100 kph [62 mph] with a tolerance of ±4 kph [2.5 mph]. Test level 2 utilizes an impact speed of 70 kph [43.5 mph] with a tolerance of ±4 kph [2.5 mph]. Roadside safety hardware evaluated at test level 2 are generally intended for use on local roads and possibly, work zones.9 Roadside safety hardware evaluated at test level 1 is intended for use on “very low volume, low-speed local streets and highways.”10 116 Collision Magazine - Volume 13 Issue 2

Figure 2: Impact Configuration for NCHRP Report 350 Test 30 Test 30 utilizes a small car weighing 1,800 lb.; whereas, MASH has increased the weight of the small car to 2,420 lb. for this test. At test level 3, the impact speed is 62 mph.

Test 30 is primarily intended to evaluate occupant risk and vehicle trajectory criteria and the vehicle should be offset to the side which will result in the greatest occupant risk during and subsequent to impact.12 For these reasons, the impact is typically offset to the passenger’s side, which induces a clockwise yaw of the test vehicle and potentially exposes the instrumented H-III crash test dummy located in the driver’s seat to the upstream guardrail. An example

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of this scenario is shown in the video sequence depicted in Figure 3.

Test 31 is conducted with the vehicle approaching parallel to the roadway [0-degree impact angle], with the impact at the test vehicle’s centerline shown in Figure 4.

Test 31 utilizes a full-size pick-up weighing 4,400 lb.; whereas, MASH has increased the weight of the full-sized pick-up to 5,000 lb. for this test. At test level 3, the impact speed is 62 mph.

For energy-absorbing end terminals designed to decelerate the vehicle to a stop, Test 31 is primarily intended to absorb the entire kinetic energy of the vehicle in a safe manner.15 For other types of devices, Test 31 is primarily intended to evaluate occupant risk and vehicle trajectory.16 The video images shown in Figure 5 depict NCHRP Test 31 at test level 3. The energy-absorbing terminal performed as intended and extruded approximately 48 feet of W-beam rail before coming to a stop.

Tests 32 and 33 are conducted with the vehicle approaching at a 15-degree angle relative to the orientation of the

Figure 3: Video Sequence of NCHRP Test 30 at Test Level 3 – Energy Absorbing End Terminal 13

Figure 4: Impact Configuration for NCHRP Report 350 Test 31 14 roadway [normal direction of traffic], with the impact at the test vehicle’s centerline shown in Figure 6.

Test 32 utilizes a small car [1,800 lb.] and Test 33 utilizes full-size pick-up truck [4,400 lb]. MASH increases the weights of these test vehicles as previously described. Tests 32 and 33 are primarily intended to evaluate occupant risk and vehicle trajectory criteria.19

Figure 5: Video Frames of NCHRP Test 31 at Test Level 3 – Energy Absorbing End Terminal 17

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Figure 6: Impact Configuration for NCHRP Report 350 Tests 32 and 33 18

Figure 7: ET-Plus Test 32 – Impact Angle Modified to 5 Degrees 21

End Terminals End terminals can be classified in two ways: 1) whether the end terminal is an energy-absorbing terminal or non-energy-absorbing terminal and whether the end terminal is installed parallel to the roadway [tangent] or 2) whether the end terminal tapers away from the roadway [flared]. The first classification addresses how the terminal behaves in a crash, whereas the second classification addresses how the terminal is installed. Appendix A provides a table showing some of the most common end terminal installed on the National Highway System [NHS]. All of the end terminals listed in Appendix A are NCHRP Report 350 test level 3 compliant. The MSKT and the SoftStop are also compliant with the current MASH standard at test level 3.

Most energy-absorbing terminals consist of an impact head that is designed to slide along the rail, absorbing the vehicle’s impact energy during the process [ET-Plus, FLEAT, SKT, etc.]. The mechanism for dissipating the impact energy is typically accomplished through permanent plastic deformation of the rail when the impact head slides along the rail, as depicted in Figure 5. However, there are exceptions. Some end terminals may utilize friction [X-Lite], cable tension [X-Tension], or some other means to dissipate the vehicle’s impact energy. For most energy-absorbing end terminals, when hit head-on, the impact head is designed to slide along the guardrail, extruding the guardrail until the vehicle’s impact energy is dissipated and the vehicle has decelerated to a stop.20 However, when hit at an angle, the impact head may partially extrude the guardrail and then bend out of the way allowing the vehicle to pass behind the guardrail. This process in which the guardrail bends out of the way and allows the vehicle to pass behind the guardrail is known as gating. All terminals currently on the market are considered gating terminals. Testing has shown that gating may occur at impact angles as low as 5 degrees. Figures 7 and 8 show still images of crash testing 118 Collision Magazine - Volume 13 Issue 2

Figure 8: SKT-350 Test 32 – Impact Angle Modified to 5 Degrees 22 in which NCHRP Report 350 Test 32 was modified from a 15-degree impact angle to a 5-degree impact angle. In these tests, the impact head extruded approximately 9 feet and 16.5 feet, respectively, before the end terminal gated and allowed the vehicle to pass.

Installation and Maintenance Issues When an accident reconstructionist considers the factors that led to a crash and the injuries of the vehicle’s occupants, the reconstructionist should consider whether the performance of the guardrail end terminal during the crash may have been affected by improper installation or maintenance. A joint task force consisting of members from AASHTO and FHWA assessed the safety performance of extruding W-beam end terminals and found several factors related to installation and maintenance adversely affected the performance of W-beam end terminals. These issues were identified by analyzing the available evidence from real-world crash involving extruding W-beam end terminals. These issues were classified into three categories:23

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1. Improper Installation/maintenance/repair

by the state DOT, and is not approved by the FHWA. Installations similar to this can and have resulted in vehicular impalements.

2. Terrain slope/grading 3. Improper end terminal placement These issues identified by the AASHTO-FHWA task force are consistent with real-world crashes investigated and analyzed by the authors, which resulted in poor in-service performance and serious injuries to the motor vehicle occupants.

Improper Installation/Maintenance/Repair Installation, maintenance, and repair issues that may adversely affect the in-service performance of the end terminal typically occur when the installer fails to comly with manufacturer specifications and/or state DOT specifications. Figure 9 shows a median end terminal for a dualfaced guardrail consisting of a blunt, non-energy absorbing end terminal [highlighted in red] coupled with a FLEAT [highlighted in blue]. This end terminal does not comply with the manufacturer’s specifications. It is not approved

Figure 9: Dual-Faced Median End Terminal Not Approved by FHWA, Not Approved by State DOT, Not Installed Per Manufacturer’s Specifications

Figure 10 shows a proper dual-faced median end terminal that utlizes the FLEAT. This installation follows manufacturer’s specifications, is approved by the state DOT, and is approved by FHWA for federal reimbursement. It utilizes two FLEAT energy-absorbing end teminals staggered 18’ – 9” apart. The second FLEAT is mounted on the fourth guardrail post and the guardrail posts are installed 6’ – 3” apart.

Figure 11 shows an ET-Plus energy-absorbing end terminal that stopped extruding because the cable anchor was bolted to the W-beam. When the ET-Plus started to slide down the rail, the vertical plate at the entrance to the feeder channel struck the cable anchor, which did not release from the W-beam as intended because it was bolted to the W-beam. [highlighted in yellow]

Figure 11: ET-Plus Improper Installation – Cable Anchor Bolted to W-Beam 25

Figure 10: Proper Installation for FLEAT Median End Terminal 24 www.collisionpublishing.com

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Figures 12a and 12b show the proper connection of the anchor cable to the W-beam. The square tube that surrounds the anchor cable is equipped with six tabs that lock into six successive square holes in the W-beam. When tension is applied to the cable, the cable anchor will lock into the W-beam with even greater force. However, if the square tube is pushed downstream [which is what happens as the ET-Plus starts to slide down the rail, the tabs get pushed out of their slots and the cable releases from the W-beam. The purpose of the anchor cable is to provide sufficient tension to the face of the guardrail in the event of an impact to the guardrail face near the end of the guardrail. When impacts occur sufficiently far enough from the end of the guardrail [about 10 posts], the posts embedded into the ground generally provide sufficient tension for the guardrail to function properly.

Another common error is commonly referred to as “Frankenstein terminals.” These installations include parts of at least two different terminals, usually done out of convenience, using whatever inventory the installer has on hand.

As mentioned earlier, a guardrail terminal is an integrated design where each component works with the rest to bring a vehicle to a safe stop. When one of those components is swapped out for different one, the results can be catastrophic. An example is shown in Figure 13, where an ETPlus W-beam rail and cable anchor are included with a SKT head. The SKT, as designed, uses a different cable attachment and a perforated first rail to reduce the initial impact forces. This Frankenstein terminal will not perform as designed and tested.

Terrain Slope/Grading Figure 14 shows an installation of an ET-Plus on northbound US-29 in Maryland between Washington and Baltimore. The bottom of the field side flange is 22 inches from the ground. In Maryland, the maximum allowable bumper height for passenger vehicles is not permitted to exceed 20 inches.26 The maximum allowable height for SUVs is 28 inches.

For the end terminal shown in Figure 14, a passenger’s vehicle bumper would strike the post before it would strike the impact plate, which would have adverse effects on its in-service performance. The underride of the front bumper underneath the impact plate would introduce vertical forces, making it more difficult for the end terminal to slide along the rail and extrude W-beam. Furthermore, the vehicle underride increases the likelihood that the ETPlus would not properly engage and lock onto the front of the vehicle and the guardraill could ride over the engine block and strike the windshield.

Figure 15 shows a lack of relatively flat graded terrain adjacent to the paved asphalt roadway. As installed, a vehicle could readily run off the roadway and down the steep terrain without ever contacting the guardrail or end terminal.

This is why terrain leading up to the terminal should be fairly flat. Also, if a vehicle struck the end terminal at an impact angle of greater than 5 degrees, the end terminal would gate and allow the vehicle to pass behind the guardrail as shown in the video sequences presented in Figures 7 and 8. In this case, more length-of-need is required. In other words, the guardrail needs to continue further upstream to fully protect agasint the steep slope.

Tabs from Anchor Cable Locked into Slots in W-Beam

Anchor Cable Attached to W-Beam

Figure 12a and 12b: Proper Installation for ETPlus Cable Anchor

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Figure 13: Frankenstein ET-Plus with a SKT Head

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Figure 14: Improper Installation for ET-Plus – Impact Plate Too High

Figure 15: Improper Installation for ET-Plus – Terrain Too Steep at End Terminal 27

Improper End Terminal Placement Figures 16a and 16b show the aftermath of a crash involving an ET-plus installed behind a curb. As the vehicle traverses the curb, the curb introduces significant vertical vehicle dynamics into the impacting vehicle. Those vertical forces would then be translated into the end terminal and guardrail, which could inhibit the end terminal’s ability to slide along the W-beam and properly extrude the W-beam and dissipate the impact energy.

Reconstruction Involving Energy-Absorbing End Terminals The reconstruction techniques presented in this section are a summary of techniques introduced by Coon and Reid in 2006.29 The purpose of the paper authored by Coon and Reid was to describe and present different types of energyabsorbing end terminals and to present a reconstruction technique for determining the initial velocity of a vehicle that struck an energy-absorbing guardrail terminal.30 The method developed and presented by Coon and Reid is based on the principles of conservation of momentum and conservation of energy.31 A summary of the techniques developed by Coon and Reid is presented in this section. The reader is encouraged to review the Coon and Reid paper to gain a more complete comprehension of the principles upon which this technique is based. This section will also present updated crash test data on energy-absorbing terminals, which has become available since Coon and Reid’s paper was published in 2006.

The Coon and Reid technique consists of five steps:

Figures 16a and 16b: Improper Installation for ET-Plus – Installed Behind Curb 28

1. Determine the vehicle’s velocity at the point the vehicle disengaged from the end terminal.

2. Calculate the energy absorbed by the end terminal.

3. Add the kinetic energy of the vehicle as it disengaged from the end terminal as calculated in Step 1 to the

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energy absorbed by the end terminal as calculated in Step 2.

(1)

4. Calculate the common velocity of the terminal head and vehicle before the terminal head starts to dissipate energy through rail deformation utilizing the sum of the kinetic energies calculated in Step 3.

where: KE=kinetic energy [ft-lb], m=vehicle mass [weight (lb.) / 32.2 (ft/s^2)] and v=vehicle speed [ft/s]

5. Using the value for the common velocity of the terminal head and the vehicle calculated in Step 4, calculate the velocity of the vehicle at impact using conservation of momentum.

Most energy-absorbing end terminals dissipate the vehicle’s impact energy by deformation of the rail as the terminal head slides along the rail. This energy is calculated by using Equation 2:

Step 1: Vehicle Speed at Disengagement from End Terminal

(2)

If the vehicle remained engaged with the energy-absorbing end terminal as it came to rest, then the kinetic energy of the terminal head and the vehicle as they started to move with a common velocity was absorbed by deformation of the rail by the end terminal. Therefore, Step 1 is not necessary and the reconstructionist may move to Step 2.

However, the vehicle will typically become disengaged from the end terminal before it comes to rest, in which case, the vehicle velocity must be determined by utilizing standard reconstruction techniques. 32,33 In the event of a rollover after the vehicle disengaged from the end terminal, the rollover distance must be determined. Appropriate drag factors34 should be applied to determine the vehicle speed at the start of rollover. Once the vehicle speed is determined at the point the vehicle disengaged from the end terminal, the kinetic energy is calculated using Equation 1: End Terminal BEAT; BEAT-MT; BEAT-SSCC BEST-350 ET-2000 ET-Plus FLEAT-350 FLEAT-350 MT

Step 2: Energy Absorbed by End Terminal

where: Erail=energy dissipated by end terminal [ft-lb], Favg=average force required to move the terminal head along the rail [lb], d=length of rail upon which the terminal head moved [ft] The average force required to move the energy-absorbing terminal head along the rail is provided in Table 1, which lists physical and mechanical properties of various energyabsorbing end terminal heads. The forces listed in Table 1 are based on instrumented crash tests, typically NCHRP Report 350 Tests 30 and 31 conducted at test level 3. The force value for the ET-2000 has been corrected. Coon and Reid originally presented an average force range of 12,00021,300 lb. Review of these tests upon which these values are based revealed that the vehicle was not brought to a complete stop when the terminal head stopped extruding/ flattening the W-beam rail. Consequently, the high end of this range [21,300 lb.] appears to be erroneous. The lower range [12,000 lb.] appears to be accurate and is listed in Table 2.

Impact Plate Dimensions [in.]

20 x 20

Weight [lb.]

130

20 x 24 20 x 20.5 15 x 28 14 x 19.6 14 X 19.6

275 268 175 120 120

REGENT 18 x 18 SKT-350 20 x 20 WY-BET; BY-BET[MB] 20 x 20

46 172 126

Average Rail Deformation Force [lb.]

Stage 1: 20,000 – 32,100; Stage 2: 29,000 18,700 – 22,500 12,000 12,500 – 15,500 13,500 – 16,700 1st Head: 13,500 – 16,700; Both Heads: 17,000 – 33,400 N/A 10,200 – 15,200 35,000

Table 2: Physical and Mechanical Properties of Energy-Absorbing End Terminal Heads 122 Collision Magazine - Volume 13 Issue 2

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At the time Coon and Reid presented their reconstruction techniques for energy-absorbing end terminals in 2006, there were no ET-Plus crash tests available to the authors for analysis. Coon and Reid recommended that the average force values for the ET-2000 be used for the ET-Plus.

Since Coon and Reid’s publication in 2006, Southwest Research Institute [SWRI] has conducted two full test matricies for end terminals outlined in NCHRP Report 350 [Tests 30, 31, 32, and 33] at test level 3. SWRI conducted these tests with the ET-Plus installed on a 27-inch guardrail and a 31-inch guardrail, resulting in a total of eight crash tests.

Of those eight tests, four crash tests [Tests 32 and 33 at each height] involved a 15-degree impact angle, which resulted in gating by the end terminal and the test vehicle continued past the guardrail. These crash tests were not deemed to be useful to determine the average force required to move the terminal head down the rail. Two of the tests [Test 31, 27-inch guardrail and 31-inch guardrail] resulted in bringing the vehicle to a complete stop while still in contact with impact plate of the end terminal. Test 31 with a 27-inch rail height generated an average force of 15,476 lb. to move the terminal head along the rail as it extruded/flattened the W-beam rail. Test 31 with a 31inch rail height generated an average force of 12,511 lb. to move the terminal head along the rail as it extruded/flattened the W-beam rail. Another test [Test 30 w/31-inch guardrail] resulted in lock-up of the end terminal head, which caused the downstream rail to buckle and strike the driver’s door of the test vehicle. This test is shown in Figure 3. Approximately 12-1/2 feet of W-beam was extruded/flattened, and the average extrusion force was 13,830 lb before the energy-absorbing end terminal locked up and the rail buckled.

Step 3: Summation of Vehicle Kinetic Energy [Step 1] and Energy Absorbed by End Terminal [Step 2] Once the kinetic energy of the vehicle as it disengaged from the end terminal is calculated in Step 1 and the energy absorbed by the end terminal is calculated in Step 2, these values are added together. The kinetic energy of the vehicle calculated in Step 1 combined with the energy absorbed by the end terminal calculated in Step 2 represents the kinetic energy of the vehicle and the end terminal as they achieved a common velocity just before the terminal head started to dissipate energy by deformation of the rail.

Adding these energies together is possible due to the law of conservation of energy.

Step 4: Common Velocity of Vehicle and Terminal Head before Energy Dissipation When the vehicle strikes the impact plate of the end terminal, the end terminal typically becomes embedded into the front of the vehicle and remains embedded into the vehicle during the extrusion process. Therefore, after a transfer of momentum occurs [Step 5], the terminal head and the vehicle share a common velocity during the extrusion process. The common velocity of the vehicle and the terminal head just before the terminal head started to dissipate energy by deformation of the rail is calculated by inserting the kinetic energy calculated in Step 3 into Equation 3: (3) where: Vc = common velocity of vehicle and terminal head; KEc=combined kinetic energy from Step 1 and Step 2; mveh=vehicle mass; mhead = mass of terminal head The weights of various terminal heads are listed in Table 1.

These weights must be divided by the acceleration of gravity (32.2 ft/s2) to calculate mass.

“

When the vehicle strikes the impact plate of the end terminal, the end terminal typically becomes embedded into the front of the vehicle and remains embedded into the vehicle during the extrusion process.

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Step 5: Impact Velocity of Vehicle

Step 3:

Because the impact plate of the end terminal typically embeds itself into the front of the vehicle during the impact, the impact between the energy-absorbing end terminal and the vehicle is considered to be inelastic with a corresponding coefficient of restitution of zero. When the coefficient of restitution is sufficiently low, the collision may be considered to be inelastic with a high degree of accuracy.35 As such, conservation of linear momentum is applied to calculate the impact velocity of the vehicle. The impact velocity of the vehicle is calculated using Equation 4:

The departing energy from Step 1 [0 ft-lb] is added to amount of energy dissipated by the end terminal in Step 2 [257,400 ft-lb.] to determine the initial kinetic energy of the vehicle and the end terminal head immediately before the end terminal head started to dissipate energy by extruding/deforming the rail.

(4)

KEc=KEdeparting+Erail=0 ft-lb+257,400 ft-lb=257,400 ft-lb Step 4: The common velocity of the end terminal head and the vehicle is calculated using the kinetic energy calculated in Step 3 [257,400 ft-lb].

Example Problem An example problem is presented for the benefit of the reader to gain a fuller understanding by presenting a practical application of this reconstruction method. This example problem was originally presented by Coon in Reid in SI units.36 The same example problem is presented in this paper in English units.

Step 5: Applying conservation of momentum to determine the initial velocity of the vehicle yields:

This example problem is derived from a full-scale crash test conducted by SWRI. The energy-absorbing end terminal was a FLEAT and the test was NCHRP Report 350 Test 30 [1,800 lb. small car, 0-degree impact angle, ¼ vehicle width offset] conducted at test level 3 [62 mph].

The measured impact speed was 62.1 mph, which yields a a variance of 3% from the actual measured test value.

The total vehicle weight, including the non-instrumented dummy was 1,997 lb. and the corresponding mass was 62.1 slug. The impact angle was 0 degrees, and the impact speed was 62.1 mph [91.1 ft/s]. Eighteen feet of rail was extruded.

In their original presentation of this reconstruction technique for energy-absorbing guardrail end terminals, Coon and Reid noted a limitation to the procedure and described it as follows:

Limitations

“Catching in the feed chute will also induce energy dissipation in downstream guardrail posts and in the guardrail beam itself. Because of these additional energy losses, this procedure is limited to impacts where the guardrail feeds freely through the chute of the impact head.” 37

Step 1: The velocity of the vehicle as it separates from the end terminal must be determined. In this case, the energyabsorbing end terminal remained in contact with the front of the vehicle until the vehicle came to a stop. Therefore, KEdeparting=0 ft-lb.

Step 2: The amount of energy dissipated by the end terminal head is equal to the force required to move the end terminal head along the rail multiplied by the length of deformed/ extruded rail. Referring to Table 1, the average rail deforming force for the FLEAT-350 is 13,500 – 16,700 lb.

For this example, an average extrusion force of 14,300 lb. will be used. In this test, 18 feet of rail was extruded.

Therefore, the dissipated energy was 257,400 ft-lb.

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This method assumed that the end terminal did not lock up while the guardrail was being extruded/deformed. The following factors were cited that could occur in the event of a lock-up that were not considered in their methodology for reconstruction: •

Higher force levels during the lock-up

•

Subsequent vehicular contact with downstream guardrail posts

•

Buckling of downstream guardrail

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•

Vehicle crush from subsequent guardrail-to-vehicle interaction.

In SWRI Test #8 [ET 31-30], the rail jammed in the extruder and locked up while the vehicle continued to buckle the downstream rail and knock down guardrail posts, thereby accounting for these other components of energy dissipation specifically mentioned by Coon and Reid. This crash test is shown in Figure 3. The average deceleration from the point where the head locked up and stopped extruding the rail to the point of final rest was approximately 1.83 g. It should be noted that this test involved a small car with a low vehicle weight. [1,791 lb.] Accounting for the weight of the vehicle, the post-lock-up deceleration force was 3,300 lb.

Conclusions End terminal crash tests outlined in NCHRP Report 350 were presented. This included the test vehicles, impact severity/speed, and impact configurations. The end terminals that are compliant with NCHRP Report 350 test level 3 were presented. Typical maintenance, installation, and repair issues that may adversely affect the in-service performance of energy-absorbing end terminals were addressed.

Lastly, reconstruction techniques for energy-absorbing guardrail end terminals originally developed by Coon and Reid were summarized. Additional NCHRP Report 350 crash test data at test level 3 has become available since Coon and Reid’s publication in 2006. This additional data supplemented Coon and Reid’s original data. This supplemental data included: •

Correction of average extrusion force data for the ET2000

•

Addition of average extrusion force data for the ETPlus

•

Quantifying energy losses and deceleration forces in crashes involving lock-up of the end terminal which led to subsequent guardrail post damage, guardrail buckling, and guardrail-to-vehicle damage.

References 1-3, 20: US Department of Transportation – FHWA. “Guardrail 101.” https://www.fhwa.dot.gov/guardrailsafety/guardrail101.pdf 4: US Department of Transportation – FHWA. https:// www.fhwa.dot.gov/legsregs/directives/policy/ra.htm.

5: US Department of Transportation – FHWA. May 26, 2017. https://safety.fhwa.dot.gov/roadway_dept/countermeasures/reduce_crash_severity/openletter052617.pdf 6, 8-12, 18-19: Ross, H. E. et al. “Recommended Procedures for the Safety Performance Evaluation of Highway Features.” National Cooperative Highway Research Program Report 350. Transportation Research Board – National Research Council. Washington, D.C., 1993.

7: Manual for Assessing Safety Hardware – Second Edition. American Association of State Highway Transportation Officials. 2016.

13-17: US Department of Transportation – FHWA. “Retesting of the ET-Plus.” https://www.fhwa.dot.gov/ guardrailsafety/retesting.cfm 13-16, 21-22: Virginia DOT. Guardrail Safety. http://virginiadot.org/info/guardrail_safety_skt_sp_350.asp 23, 25, 27-28: AASHTO/FHWA. “Safety Analysis of Extruding W-Beam Guardrail Terminal Crashes.” September 2015.

24: Florida DOT. Approved Products List – FLEATMT-MGS. https://fdotws1.dot.state.fl.us/ApprovedProductList/Products/Index/697.

26: American Automobile Association. “Digest of Motor Laws – Bumper Height.” https://drivinglaws.aaa.com/ tag/bumper-height/ referenced August 2018.

39-31, 35-37: Coon, B. and Reid J. “Reconstruction techniques for energy-absorbing guardrail end terminals.”

Accident Analysis and Prevention. Volume 38, 2006. Pp. 1-13.

32: Fricke, L. Traffic Accident Reconstruction – Volume 2 of The Traffic Accident Investigation Manual, Northwestern University Traffic Institute.

33: Daily, J. et al. Fundamentals of Traffic Crash Reconstruction. Institute of Police Technology and Management. 5th Printing. January 2013.

34: Stevens, D. et al. Rollover Crash Test Results: SteerInduced Rollovers.” SAE Paper 2011-01-1114.

38: United States Department of Transportation – Federal Highway Administration. https://safety.fhwa.dot.gov/ roadway_dept/countermeasures/docs/RoadTerminals_ Nov2015Safelogo.pdf

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Appendix A – Typical NCHRP Report 350 Test Level 3 End Terminals End Terminal Buried-inBackslope

Performance Installation Type Type Non-Energy Flared Absorbing

Regent-C

Non-Energy Flared Absorbing

Eccentric Loader Terminal

Non-Energy Flared Absorbing

Slotted Rail Terminal [SRT]

Non-Energy Flared Absorbing

Flared EnergyAbsorbing Terminal [FLEAT]

EnergyAbsorbing

Flared

Trend 350

EnergyAbsorbing

Flared

Sequential Kinking Terminal [SKT-350]

EnergyAbsorbing

Tangent

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ET-Plus

EnergyAbsorbing

Tangent

SoftStop

EnergyAbsorbing

Tangent

X-Tension

EnergyAbsorbing

Tangent

X-Lite

EnergyAbsorbing

Tangent

Wyoming BoxBeam End Terminal [WY-BET]

EnergyAbsorbing

Tangent

Bursting Energy absorbing Terminal [BEAT]

EnergyAbsorbing

Tangent

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Toyota Gen1 EDR Event Recording Logic

Case Problem Solution W. R. Rusty Haight, Collision Safety Institute Robert Anderson, Biomechanics Analysis

A

s noted, this car was used in a series of crash tests which were part of a larger research project conducted in conjunction with the Texas Department of Public Safety, Highway Patrol State Crash Reconstruction Team. When the project started, the 06 Corolla had one recorded trigger/event: “TRG 1.” The car was used as part of the larger project to examine recording order and prioritization as detailed in the paper “Toyota Gen1 EDR Event Recording Logic” found in this issue of Collision.

In addition to the series of tests adding to the database of progressive recording examples developed for the recording logic article, one of the goals of the testing was to evaluate the statement found in the Data Limitations text (05002_ToyotaTRW_r027) for this module type which reads: “... “TRG Count” indicates the number of frontal/ rear recording triggers that have been established.

The calculated value does not include the number of times side or rollover recording triggers have been established. The sequence in which each frontal/ rear event occurred can be verified from the “TRG Count”. The lesser the “TRG Count” value, the older the data. The upper limit for the recorded value is 254 times. When more than one event reaches the upper limit, the actual “TRG Count” may be greater than what is displayed for that event. ...” (emphasis added) While this statement is further addressed in the article (“Toyota Gen1 EDR Event Recording Logic”), a large part of this specific test series was designed to narrowly focus on the maximum TRG count and what would actually happen once that maximum number was reached. That there was a discussion of the maximum number included in the Data Limitations, it would seem it might be anticipated that that number might, at some point, be reached. In reports from the field, CDR Tool users have shared examples of Toyota vehicles from which crash data was retrieved in128 Collision Magazine - Volume 13 Issue 2

cluding “real world” trigger counts exceeding 90 for vehicles still in use. Moreover, in later Gen3 systems, the Data Limitations text indicates that the maximum number was increased from 254 to 65,533 as seen in this passage (again from 05017_ToyotaS00std_r027): “... “TRG Count” indicates a calculated value of the number of times recording triggers have been established for all crash types. The sequence in which each event occurred can be verified from the “TRG Count”. The smaller the “TRG Count” value, the older the data. The upper limit for the recorded value is 65,533 times. When more than one event reaches the upper limit, the actual “TRG Count” may be greater than what is displayed for that event. ...”

Returning attention to the module type associated with this particular case problem, this part of the testing was referred to as the “How many licks does it take to get to the middle of the Tootsie Pop?” series or, what really happens when (if ) we set 254 triggers? An extension of that would be to evaluate whether or not the module behavior would follow the predicted path as seen in the recording logic article.

The test series conducted followed the pattern set out in the recording logic article from TRG1 through TRG124.

After that, the series was continued uninterrupted until reaching TRG254. From the recording logic article, we know that, when recording a new event, the Gen1 module protects the chronology of the “most recent event” and records the newly occurring event’s data over either a previously recorded and unprotected positive delta-V event or the smaller magnitude event of two events with the same polarity. Armed with that information, the 4th TRG set in the test series (identified as TRG4) was designed to have a negative longitudinal delta-V event and then all subsequent events created to increase the trigger count were conducted so as to result in positive longitudinal delta-Vs. That way, TRG4 would be preserved up through TRG254. Figure

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1 shows where TRGs 4, 16 and 17 had been recorded and the data retrieved; TRG4 being that last negative longitudinal delta-V and, as subsequent events were created, here TRGs 16 and 17, they were all positive longitudinal deltaV events leaving TRG4 “undisturbed.”

Figure 2 shows the Record Summary listed events as retrieved after Triggers 123 and 124. In this case, TRG4 remains “protected” again by virtue of the way the tests were planned and these two more recent events were conducted close in time to one another (3.953 sec. apart) such that the order could be confirmed by more than just the trigger count value.

At Figure 3, the Record Summary data table shows the currently recorded events are Triggers 4, 254 and 254. Notably, both of the events listed as TRG254 in that table are marked “Most Recent Front/Rear Event.” The longstanding TRG4 is here marked “1st Prior Frontal/Rear Event.”

Prior to this point in the series, TRG4 had been marked as “Prior Frontal/Rear Event” (see Figure 2).

Consistent with what we find in the Data Limitations text and the recording logic paper, as shown in Figure 3, the trigger count has reached its maximum count (“... The upper limit for the recorded value is 254 times. ...”). [Editor’s note: the fuller process is detailed in the paper “Toyota Gen1 EDR Event Recording Logic” found in this issue of Collision.]

The test series continued and, using the knowledge of the process as detailed in the Recording Logic paper, TRG4 was ultimately intentionally overwritten. After that, the data found in the Case Problem statement was retrieved and the recorded events are described as seen in Figure 4 (as seen in the data tables from the Case Problem Statement found earlier in this issue of Collision).

It is a given that you may adopt as fact, for the current investigation, that this Toyota was struck from behind at a relatively low speed by another vehicle and then data retrieved. The questions and then answers for this case problem are offered here:

Figure 1: Event Record Summary at Retrieval showing TRG Count 17, 16, and 4

Figure 2: Event Record Summary at Retrieval showing TRG Count 124, 123 and 4

Figure 3: Event Record Summary at Retrieval showing TRG Count 254, 254 and 4 www.collisionpublishing.com

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Figure 4: Event Record Summary at Retrieval showing TRG Count 254, 254 and 254 1. Explain why the TRG (trigger) count numbers are all identified as TRG 254, Most Recent Event.

The Data Limitations text for this module type includes, as previously indicated, the passage: “... The upper limit for the recorded value is 254 times. When more than one event reaches the upper limit, the actual “TRG Count” may be greater than what is displayed for that event. ...”

That may be interpreted to read that, once TRG 254 is recorded, the trigger count value will not increase and that any events which may occur and be recorded after TRG 254 is reached, will continue to be identified as TRG 254 although the actual count, if one were able to establish that, may be higher. Undeniably, a given car being 254 events in the real world is unlikely; however, given the right circumstances (outside testing) it is anticipated by the module design and functionality.

The observation that the module identified all three recorded events as TRG 254 (as seen in Figure 4) is consistent with the description given in the Data Limitations text.

What is not addressed in the Data Limitations text is the observation that all are also identified as the “Most Recent Frontal/Rear Event.” Another observatioaan which may be made is that the display order, in the “Front/Rear Event Record Summary at Retrieval” data table is based on recording page; “Page 0,” then “Page 1,” then “Page 2.”

Normally the display order is based on trigger count order chronology.

By way of comparison, one might look to Figures 1 and 2 and should notice that the display order, where the chronology is properly identified and the trigger count for all triggers is less than the maximum value 254, is based on the trigger count and not the record page numbers in ascending order as seen in Figure 4.

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2. Although all three events are identified as “Most Recent Event,” if you can tell, what is the actual chronological order of the recorded events?

To answer this question, since the displayed trigger count number is the same for all three events and the assigned chronology identifier name is the same for each event (“Most Recent”) and the display order in the “Front/Rear Event Record Summary at Retrieval” data table is based on recording page number not chronology, one would have to look elsewhere in the report for clues as to the actual order, assuming they might be available. The clues identified and used here may not always be available for a given Toyota system depending on a variety of conditions; however, unless and until one reads a given data translation report completely - front-to-back - it would be impossible to know one way or the other.

Reading the translation report found in the Case Problem statement, the events are displayed in the order in which they would appear in the report as printed. That order, as seen in the “Record Summary” is based on recording page number. The first event displayed (marked as “From page 5 in the report”) is data from recording “Page 0.” The next one (marked as “From page 8 in the report”) is data from recording “Page 1.” The last set of data is from recording “Page 2" and is marked as “From page 11 in the report.”

For simplicity, they will be referred to here by recording page number.

In the data from recording “Page 0,” we find a data table for “DTCs Present at Start of Event.” That data table is included for each of the other recorded events. There are three data elements in the table as seen in Figure 5. These same elements are found in each of the other recorded event records. For this analysis we turn our attention to the first two elements: “Ignition Cycle Since DTC was Set (times)” and “Airbag Warning Lamp ON Time since DTC was Set (min).” The third data element (“Diagnostic Trouble Code (1)”) indicates that there is (was) a diagnostic trouble code (DTC) present at the time of that event and the translated code number may be used to identify

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Figure 5: DTCs Present at Start of Event (TRG 254)

Figure 6: DTCs Present at Start of Event (TRG 254)

Figure 7: DTCs Present at Start of Event (TRG 254)

Figure 8: System Status at Time of Retrieval the specific code. For our purposes here, that information isn’t important particularly since it’s the same for all three recorded events.

The first data element - “Ignition Cycle Since DTC was Set (times)” - is often mistakenly identified as the data element common to, for example, GM data sets and labeled “Ignition Cycles at Event.” But that’s not what’s being described here, this isn’t a lifetime ignition cycle count.

What’s being given here is an answer to the question: “how many ignition cycles have there been since the (indicated) DTC was set?” In Figure 5, we see that the answer is 9 ignition cycles.

Along with that, in Figure 5, we see the data element “Airbag Warning Lamp ON Time since DTC was Set (min).”

This might be read as an answer to the question: “the airbag control module has been powered on (here across 9 ignition cycles) for a total of how many minutes (at one minute resolution)?” The answer here is 77 minutes as seen in Figure 5.

One should then compare that data table (from the data at “Page 0") with the data table from “Page 1" as seen in Figure 6 and the data table from “Page 2" in Figure 7. After comparison, the obvious conclusion, given the number of ignition cycles and time the warning lamp has been on, is that the actual chronology can, in this case, be established using these data elements.

In this example, the data retrieved and offered in the Case Problem problem statement occurred (using the recording page frame of reference) in this chronological order: “Page 0" then “Page 1" then, the real “Most Recent Frontal/ Rear Event” at “Page 2.” If “ a picture is worth a thousand words,” and if the recorded events were to be given “normal” chronological event names (where the trigger count would be less than 254), the order and rationale would be as seen in Figure 9.

The event with the greatest number if ignition cycles since the DTC was set would be the oldest at the time of retrieval. What should be kept in mind is that the ignition cycle count and time data elements are for each event at the time that event was recorded.

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Collision Magazine - Volume 13 Issue 2 131

Figure 9: Front/Rear Event Record Summary - Adjusted Going back to the paper “Toyota Gen1 EDR Event Recording Logic” found earlier in this issue of Collision, the normal recording order would be to record first occurring event to “Page 0" then to “Page 1" and then back to either “Page 0" or “Page 2.” Over time, as seen in Figures 1 and 2, given the polarity and magnitude of different events, the chronological order and the recording page order bear little resemblance.

In this instance, at this point in the recording series, the chronological order established using the “Ignition Cycle Since DTC was Set (times)” data element has no direct relationship to the recording page numbering; an important concept to bear in mind given the next couple questions.

In the Case Problem statement, we were told we may assume that, after the minor crash which lead to the recorded data as shown, the driver of this car was involved in another crash. In that subsequent crash, this driver was again rearended by another car. You may adopt as fact that the longitudinal delta-V in the subsequent crash was about +6.1mph.

3. Can you say whether or not there would there be any new data (a new recorded event) as a function of this subsequent rear end crash?

The answer to the question as posed is simply “yes.” That is to say, given the information we have from the data translation report which is offered in the Case Problem problem statement, yes, we can say whether or not new data would be recorded as a function of the more recent crash as described (the subsequent crash with the +6mph longitudinal delta-V).

In the simplest terms, the basis for the yes. answer is found in the Data Limitations text for this module, where we find this passage: “... The “ON” setting for the “Freeze Signal” indicates a state in which the non-volatile memory can not be overwritten or deleted by the airbag ECU. Af-

132 Collision Magazine - Volume 13 Issue 2

ter “Freeze Signal” has been turned ON, subsequent events will not be recorded. ...”

Essentially, what that means is, if the “Freeze Signal” data element indicator in the data translation report is set to “ON,” then the module is “locked” and no other events can be recorded. Conversely, if it’s NOT set to “ON” - it would be set to “OFF” - then the module can continue to record new events following the flow chart found in the recording logic paper. Here we find, in the “System Status at Retrieval” data table, that the “Freeze Signal” is “OFF” as seen in Figure 8.

While some consideration might be given to the magnitude of the delta-V of the subsequent event as to whether or not that would or might play into the potential for a subsequent event to be recorded, as detailed in the recording logic paper, for this type of module, once the airbag control module senses a crash meeting the trigger threshold, assuming there’s available power, the data is going to be recorded; there is no minimum delta-V requirement.

Adopting that the “Freeze Signal” is “OFF” as reported, the remaining question is: what recording page will the new event be recorded to or which previously recorded event will be overwritten?

4. If there was new data recorded as a function of the subsequent crash, which previously recorded “Most Recent Event” would be overwritten by data from this subsequent event?

Referring back to the recording logic flow chart seen in the paper “Toyota Gen1 EDR Event Recording Logic” found in this issue of Collision, when the module goes to record the next occurring trigger, it would first protect the “Most Recent Event” and then look at the other recorded event(s). Of those one or two events, if one has a positive and the other a negative delta-V, the priority is to retain the negative delta-V so the positive event would be overwritten. If both have the same polarity, then the priority would be to preserve the greater magnitude delta-V event and overwrite the event with the smaller delta-V.

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Figure 10: DTCs Present at Start of Event (TRG 254)

Figure 11: Front/Rear Event Record Summary - Adjusted But here, all three recorded events are at least marked the “Most Recent Event” so the first step - that being to protect the “most recent event” - seems to be virtually impossible without the manual analysis done as seen in Figure 9. Another way of saying that is: the module is calling all three events “most recent,” so when the module needs to protect one of them, prevent that one from being overwritten, what criteria would be used to make that determination? That leads to the next question in this case problem.

During the test series done to create these event records, the next test was a rear-to-front impact resulting in a +6.1mph delta-V as described in the problem statement. Referring back to the recording logic flow chart, the event recorded in “Page 2" should be the actual “Most Recent Front/Rear Event” (see Figure 9) and, as such, should be protected by the module when the next event is to be recorded.

The module should then look at the two remaining events, recognize that they both have a negative longitudinal deltaV; however, the magnitude of the event recorded at “Page 1" is the smaller of the two. For that reason, the newly created event should be recorded to “Page 1.”

To check that supposition, another crash tests was conducted and, of course, the data was retrieved then, as before, the information contained in the “DTCs Present at Start of Event” data table can be examined to establish event order. In the translation report created by the CDR Tool software, that data table for the events recorded in Pages 0 and 2 remained the same; however, the information for “Page 1" changed as seen in Figure 10.

It becomes clear then that the module followed the anticipated recording logic as described in the “Toyota Gen1 EDR Event Recording Logic” paper found in this issue of Collision and, to do that, had to somehow identify the actual recorded order to protect the “Most Recent Event” when recording subsequent event(s). Although unconfirmed officially by anyone with Toyota, the most logical explanation would be that when an event is recorded (and identified by whatever trigger count number) it is flagged to be protected as the “Most Recent Event” if or when a subsequent event is to be recorded.

In other words, a flag in the data protecting the “Most Recent Event,” when it is recorded is applied dynamically such that when the next occurring event is to be recorded it is protected and then that flag is applied to the subsequent event when it is recorded and that later event becomes the newer “Most Recent Event.” This methodology would not rely on an assigned trigger count number and would continue to function even after the maximum number of trigger counts is reached. Naturally that would have to be tested ... as seen below in question 5.

Another interesting observation is that the time element for how long it had been since the DTC was set appears to be “stuck” (have reached a maximum value?) at 92 (minutes). There is no guidance in the Data Limitations text on this point. However, as a function of continued testing in this series, the observation made is that, no matter how long the module was powered up between tests to include time to image data, once the timer reached 92 min, it didn’t increment higher (see Figures 6, 7 and 10).

The actual chronological order, using the method applied to create Figure 9, is repeated and offered here as Figure 11. www.collisionpublishing.com

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Figure 12: Front/Rear Event Record Summary - Adjusted 5. If there was new data recorded as a function of the subsequent crash, what would the TRG number of the new event record be?

The answer to this question, as posed would simply be “254” (TRG 254). We know, from the Data Limitations text that “...The upper limit for the recorded value is 254 times. When more than one event reaches the upper limit, the actual “TRG Count” may be greater than what is displayed for that event. ...”. For that reason, to answer the question, as posed, any new event recorded - until the “Freeze Signal;” is set - would be 254.

As noted in the answer discussion for question 4, this begs the question: when a subsequent event is to be recorded, how does the module choose which recording page it would record that data to? As noted previously, although unconfirmed officially by anyone with Toyota, the most logical explanation would be that when any new event is recorded (and identified by whatever trigger count number) it is flagged so that it would be protected as the “Most Recent Event” if or when a subsequent event is to be recorded.

In other words, a digital flag is applied to the data for the last recorded event protecting it as the “Most Recent Event,” when it is recorded. When the next occurring event is to be recorded that event is protected and then, when the subsequent event is recorded, that flag is cleared from what becomes the “First Prior event” and applied to the subsequent event such that it then becomes the newer “Most Recent Event.” This methodology would not rely on an assigned trigger count number and would continue 134 Collision Magazine - Volume 13 Issue 2

to function even after the maximum number of trigger counts is reached.

To prove this out, in the underlying test series, additional crashes were conducted and to further examine the process, the next three tests were staged to occur within 5120ms (a limit to the value for the recorded time between events as defined in the Data Limitations text). All three were designed to result in positive longitudinal delta-V. After the test sequence, data was retrieved and, comparing the previous set of data with the subsequent one and examining ignition cycles since DTCs were last sent and onto what memory page the data was recorded, it becomes clear that the airbag control module does know what the actual “Most Recent Event” would be, dynamically, when it looks to protect that event and record a subsequent event(s).

In Figure 12, the information from Figure 11 may be compared, now to include the reported time between events.

In the “Front/Rear Event Record Summary” data table, the time is reported as “N/A” once TRG 254 has been reached.

However, in the “System Status at Event” for each individual event record, we find the data element “Time from Previous TRG (ms)”. There, one may find the recorded time between events from that event back to the previous is reported (to a maximum of “5120ms or greater.”

The first observation is that the longer standing event with a -4.7mph delta-V remains in place (for reference, it is the “Page 0" event identified as TRG 254+n). Since the subsequent three events would all be positive longitudinal events, the memory page they are recorded to would, af-

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ter loosing true “Most Recent Event” protection, be available for subsequent event recording.

At the beginning of this sequence, what we will call TRG 254+n+3 would have been the true “Most Recent Event” at “Page 1" and would be protected.

For that reason, what would be then TRG 254+n+4 would not overwrite the negative longitudinal data at “Page 0" and the airbag control module’s EDR function would have to choose between memory Pages 1 and 2. If “Page 1" is actually the true “Most Recent Event,” then the remaining option would be to record this event (“real” TRG 254+n+4) to “Page 2.”

Editor’s note: Lt Aaron Fritch and Sgts C. J. Villareal and Jason Rolison of the Texas Department of Public Safety, Highway Patrol’s State Crash Reconstruction Team and their team members worked tirelessly to help plan and conduct the sequence of crash tests (and others) which lead to the data discussed in this analysis.

The next impact was 2.458s later and resulted in a +3.2mph delta-V. As before, what would now be TRG 254+n+5 would not overwrite the negative longitudinal data at “Page 0" and the airbag control module’s EDR function would again have to choose between memory Pages 1 and 2. If the choice was unpredictable, where the data would be recorded would be effectively random; however, there must have been a flag/indicator associated with the event recorded at “Page 2" (TRG 254+n+4) such that it would be the true “Most Recent Event” and the module would then write this next event to memory “Page 1.”

The third and final impact in this sequence was 3.277s later and resulted in a +4.9mph delta-V. As before, what would now be TRG 254+n+6 would not overwrite the negative longitudinal data at “Page 0" and the airbag control module’s EDR function would again have to choose between memory Pages 1 and 2. As before, there must have been a flag/indicator associated with the event recorded at “Page 1" (TRG 254+n+5) such that it would be the true “Most Recent Event” and protected and the module would then write this next event to memory “Page 2.”

What had been at memory “Page 2" would have, at that moment, been the “First Prior Event:” it was no longer protected and the page available for recording.

The data as recorded before this final sequence (with time between events added) is seen in Figure 12. Below that, in Figure 12, is the actual test sequence for the three impact sequence. At the bottom of Figure 12 is the adjusted sequence based, not on memory page number but the true trigger count illustrating that the module, even after TRG 254, actually does follow the logic flowchart seen previously in this issue of Collision.

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Collision Magazine - Volume 13 Issue 2 135

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