Volume 11, Issue 1

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Soil-Tripped Rollover Crash

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Contents

2016

Volume 11 Issue 1

inside 06

08

Letter From the Editor

features 08

The Accelerations Present During the Trip Phase of a Soil-Tripped Rollover Crash by: Nathan A. Rose and Neal Carter

32

The Older Lady Versus the Younger Lady: Female Occupant Kinematics in Low-Speed Rear End Collisions by: Lissette M. Ruberté, Billy S. Cox, Jr. and Susan Lantz

48

My Turn at the Wheel: Be Careful With What You Ask For It Might Bite You! by: Erik Carlsson

58

Streamlining Accident Investigation by: Larry Trojak

62

Using Ford EDR Pre-crash Stability Control System Data by: John Bruno and Richard R. Ruth

74

How Accurate are Witness Distance Estimates Given in Car Lengths? by: Nathan Rose, Neal Carter, John Kreisher, Martin Randolph, William Neale and David Danaher

88

Release History of Bosch Crash Data Retrieval Tool Software by: W. R. Rusty Haight

32

62

88

case study 24

Crash Data Case Problems by: W. R. Rusty Haight

98

Case Problem Solutions www.collisionpublishing.com

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Industry Partners IAARS

is one of the oldest associations dedicated to expanding the knowledge and training of automobile crash Reconstructionists. IAARS was started in 1980 as a resource for members to use when the inevitable questions arose while trying to reconstruct a crash. We have members from several different countries as well as many different states across the U.S. The annual seminars not only provide instruction on specific topics by experts in the field, they provide face-to-face discussions on how other people approach reconstructing a crash. The old saying "two heads are better than one" is definitely true in accident reconstruction.

Membership in IAARS has other advantages, including a discount (from IAARS) on a subscription to Collision Magazine; access to electronic delivery of the quarterly publication of the Society of Accident Reconstructionists, and electronic delivery of bi-monthly editions of Accident Reconstruction Journal. Members also receive discounts at our training seminars.

We have annual conferences with training opportunities across the country. IAARS was the first association to do actual crash testing in 1982 with T-Bone and Head-on collisions and we continue to try and have a dynamic full scale vehicle crash or testing at each of our seminars. As a member you have full access to the setup and test with all of its data. As a member you have We have three levels of membership. Our highest level, Felaccess to a vast archive of videos and data from many different low, includes a peer review of a reconstruction project. This and sometimes unique tests. Some of these tests include vehicle differs from many other associations in that with this process for membership it can help the member as peer review is a spe- to vehicle, truck to vehicle, motorcycle to vehicle and vehicle to pedestrian crashes, semi rollovers, tip-over testing and brake cific area of judicial consideration when admitting an expert opinion in court. This review, along with training and experi- efficiency testing. IAARS sometimes partners with other associaence, go a long way if you happen to come across a stickler for tions when providing seminars or conferences. a judge. We offer opportunities for a member to publish work IAARS enjoys working with all of the associations and organizaor research in the reconstruction field and an opportunity to tions that are involved in helping get answers to how and why present materials at our annual conferences. collisions occur and how to make our roads and highways safer.

Hope to see you at one of the conferences.

Our association has "old-timers" that have worked thousands of accidents. If you have a question, chances are they have Sincerely, encountered a similar situation in the past and can give some Fred Rice insight on how they worked it out. We also have members very President, IAARS familiar with the ins and outs of new technology (Crash Data www.iaars.com Retrieval, Photogrammetry) and are willing to help with any questions or concerns you may have.

The ARC Network

was founded in 1998 and since has grown to be the largest and most respected organization for Accident Reconstruction and Traffic Investigation.

Stiffness spreadsheet program. Extend your savings and sign up as a Police Agency member or Platinum member and receive a complimentary subscription to Collision Magazine!

The ARC Network is more than just an organization it is a research and communications Internet portal for everything accident reconstruction. The ARC Network provides research tools, Expert directory, event calendar, industry news feeds, education directlory and a Member's Only area that gives access to various online databases and information.

In addition, as a member you have the choice of being listed in the most-visited Expert Directory on the Internet. Our Expert directory boasts a #1 ranking in Google searches (a feat worth much more than the cost of membership)!

The ARC Network offers various membership levels to fit your needs and budget. Membership starts at just $89.00 per year.

However, that is where the value of the ARC Network starts.

With your membership you not only get access to everything in the ARC Network, you also receive a, $50 discount to the annual ARC-CSI Crash Conference, a $50 discount to the CDR User's Summit and a free copy of Greg Russell's Excel Crush

Scott Baker President The ARC Network (800) 280-7940 arc@accidentreconstruction.com

www.accidentreconstruction.com

For information on becoming a Collision Industry Partner send an email - sbaker@collisionpublishing.com 6 Collision Magazine - Volume 11 Issue 1

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ADVERTISING RATES & INFO If you would like to advertise your products or services in Collision Magazine, rates, availability and other information are available online at: www.collisionmagazine.com

Collision Publishing Inc PO Box 892885, Temecula, CA 92589 Toll Free: 800-280-7940 E-mail: sbaker@collisionpublishing.com Web: www.collisionmagazine.com ISSN: 1934-8681

COLLISION STAFF Scott Baker Sean Haight Tonya Baker Carla McLeod

Owner, Managing Editor Senior Editor Advertising Manager Subscription Services

All rights reserved, © 2016 Collision Publishing Inc. The opinions and conclusions expressed in this publication and attached data disk 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. Dis-

senting opinions, discussion or conclusions which may express an adverse position to those expressed herein by specifically cited authors can be addressed in writing by sending an e-mail to the editor at sbaker@collisionpublishing.com or sending a "regular mail" letter to us at PO Box 892885, Temecula, CA 92589. By

sending correspondence to either our e-mail or snail mail addresses listed in this publication you are agreeing that: (1) we are by definition, "the intended recipient" (2) all information in the e-mail is ours to do with as we see fit and make such financial profit, political mileage, or good joke as it lends itself to and, (3) this overrides any disclaimer or statement of confidentiality that may be included on your original message. The entire physical universe, including this publication and attached data disk, may one day collapse back into an infinitesimally small space. Should another universe subsequently re-emerge, the existence of this publication and data disk in that universe cannot be guaranteed.

ADVERTISER INDEX 4N6XPRT Systems ARC Network Berla CDR User's Summit Collision Magazine Collision Safety Institute Crash Data Group EDR Experts Engineering Dynamics FARO Impact General Laser Technology Inc Leica / Microsurvey Software Northwestern CPS senseFly Visual Statement

PAGE Inside Back 105 57 72 47 97 106 46 Inside Front 73, 87 60 22-23 2, 53-56 1 61 30-31, Back

Included with this issue of Collision

is the 2016 CDR User's Summit Data DVD. This data DVD includes all the materials delivered at the 2016 CDR Summit in Houston, Texas.

SUBMIT AN ARTICLE If you would like to submit an article to be published in Collision please e-mail us your article for review or call us toll free 800-280-7940. E-mail: sbaker@collisionpublishing.com

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If you would like purchase back issues of Collision Magazine, please visit www.collisionmagazine.com or call us toll free 800-280-7940. www.collisionpublishing.com

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Letter from the editor Regular readers of Collision have probably noticed that this issue had been sent out a little later in the year than normal. There are a couple reasons. First, in yet another effort to be able to bring Collision to a larger audience, we’ve sought out printing support which is more flexible in terms of overall cost as well as to cover increasing readership - the overall volume of copies mailed out. Second, and perhaps more exciting, is that the release date of this issue moves in the direction of the new alignment of the CDR User’s Summit in Houston and the ARC-CSI Crash Conference in 2017.

For 11 years, the CDR User’s Summit has been held annually in January. While this offered some time separation between that conference and the ARC-CSI Crash Conference (normally in late May or early June in Las Vegas) input from attendees indicates that this timing makes, for example, for those in law enforcement, getting department approval for travel difficult. Coming right off the holidays and so close to the start of the fiscal year for many, some have reported that a January conference is often difficult to schedule. Then there’s the question “why Houston?”

When we decided to sponsor the first CDR User’s Conference, we looked for a geographically central location which was also an airline hub. The choice for the first Conference was Dallas. We quickly learned that the conference would be bigger than airport hotels would reasonably support in Dallas and to keep attendance costs down, we looked to Houston. Houston, again a geographically central airline hub city was also one which would normally

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be less likely to suffer from January weather related travel issues. Moreover, Houston is home to the fourth largest multiairport system in the U.S., has the nation’s third largest fleet of taxis and, of course, is home to NASA – the world’s leading space and aeronautics research center as well as a variety of other attractions nearby.

Listening closely to input from attendees and other factors, we reevaluated the timing of the Summit and that brings us to change the date of the Summit from January to March. So, for 2017 the next CDR User’s Summit will be held March 6-8, 2017.

Spring weather in Houston is generally more mild, travel issues even coming from the Northeast are less likely and, generally, airfare into and out of Houston is lower in the early spring. For that reason, the first issue of Collision for a given year - this issue - which is tied to the CDR User’s Summit, would shift from mailing in the spring to a little later; early summer.

There’s another big change in the works for the CDR User’s Summit for 2017 which would involve broadened Summit partnerships and potential addon training opportunities so keep an eye on http:// www.cdrsummit.com/ for developing news as we move closer to the Summit.

This shift in the date of the CDR User’s Summit will also mean a shift in the timing of the ARCCSI Crash Conference held annually for the past 15 years. Now the largest ongoing open crash conference in the world, the ARC-CSI Crash Conference has been held in Las Vegas in late May or early June annually for the last 14 years (2016 marked the 15th conference after the first one was held in Seattle). When we first planned the ARCCSI Crash Conference, we selected Las Vegas not as a “vacation destination” but because it gave us the greatest flexibility in terms of venues of any city we could find together with minimizing travel/hotel costs for attendees.

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While we recognize that there are some who may still have administrative issues getting approval to attend a conference in Las Vegas, when the actual volume and quality of the content of the Conference (from the crash tests to the presentations) is used to demonstrate the professional value of the Conference, we’ve seen a shift toward greater approval of travel to “Vegas.”

Hosting the ARC-CSI Crash Conference in Las Vegas for 14 years has resulted in the development of strong long-standing relationships between the Conference and local entities from the Las Vegas Motor Speedway to police agencies and those who provide the vehicles for the crash day testing.

This year, for example, Volkswagen provided us with several 2016 Passat models for testing and 5 new model motorcycles were provided by Harley Davidson which became part of the largest single set of documented motorcycle crash tests in history (watch for some of the results of the test series in the next issue of Collision later this year!).

This year, the over-hyped threat of some sort of demonstration at the conference hotel never materialized and the conference went on without any issues or concerns. That, of course, brings us back to how the shift in the CDR User’s Summit and publication dates of Collision would impact the ARC-CSI Conference in 2017.

to support a wide range of exciting crash tests for the 2017 ARC-CSI Crash Conference as well as a variety of presenters so mark your calendars now!

The crash test day will again be held on the infield of the Las Vegas Motor Speedway. With the Media Center overlooking the finish line at the speedway and giving attendees the ability to relax indoors between crashes in an air conditioned building with free WiFi and a variety of presentations, this is undeniably the top venue for a crash day associated with an event like the ARC-CSI Crash Conference.

Of course, this means the second issue of Collision for 2016 and going forward would be shipped later in the year to correspond to the later scheduling of the ARC-CSI Crash Conference. Either way, readers can continue to count on the fact that we’re working hard to meet production deadlines while keeping the content fresh, original, timely and meaningful and maintaining the relationship to the CDR User’s Summit, ARC-CSI Crash Conference and newly strengthened associations with regional professional organizations.

Managing Editor

The later date of the CDR User’s Summit means that the ARC-CSI Conference would also be held later next year. So, for 2017, the ARC-CSI Crash Conference is slated for September 18-21, 2017.

The weather - always something of a consideration in Las Vegas - is normally comparable if not cooler in late September than in late May, early June. We’re already working with auto manufacturers and others

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WE E I V

The Accelerations Present during the Trip Phase of a Soil-Tripped Rollover Crash Nathan A. Rose Neal Carter Kineticorp, LLC

I

ntroduction Figure 1 depicts the vehicle motion typical of a high-speed, soil-tripped, single vehicle rollover. The scenario depicted in this figure involves the following events: (1) a driver inputs a rightward steer of sufficient severity to cause the vehicle to enter a yaw (clockwise, in the example below); oftentimes, the steering input that leads to a loss of control will have been preceded by other steering inputs; (2) the driver may input a counter-steer back to the left, but is unable to regain control; (3) at some point during the clockwise yaw, the vehicle leaves the roadway, the wheels furrow into the soil, and the trailing side tires lift off the roadway (the passenger’s side tires in the example below); (4) as this motion continues, the center of mass travels over the leading side tires, the leading side tires lift off the roadway, and the vehicle becomes airborne and begins rolling; (5) the vehicle rolls until its kinetic energy is dissipated and it comes to rest.

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A rollover crash like this one is often split into three phases for analysis – the loss of control phase or yaw phase; the trip phase; and the roll phase. During the loss of control phase, driver steering inputs – or perhaps some other external force – result in the driver losing control of the vehicle. The vehicle enters an unrecoverable yaw during this phase. The trip phase then begins when the trailing side tires of the vehicle lift off the ground. This phase ends when the leading side tires also lift off and the vehicle becomes airborne [Orlowski, 1989]. A number of mechanisms can cause a vehicle to trip and begin to roll.

For example, a combination of tire forces and suspension effects from severe steering inputs [Larson, 2000; Wilson, 2007a and 2007b; Stevens, 2011], interaction between a tire or wheel rim and pavement [Marine, 1999], wheels furrowing into soil or sod [Cooperrider, 1990 and 1998; Asay, 2009 and 2010], and wheels impacting a curb [Jones, 1975; Cooperrider, 1990; Hughes, 2002]. The roll phase begins once the vehicle becomes airborne following the trip phase and continues to rollover until it comes to rest.

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Figure 1: Typical High-Speed, Single Vehicle Rollover Crash Reconstructing the initial speed of a vehicle involved in this type of three-phase rollover involves determining the distance over which each phase of the crash occurred and then determining how the vehicle decelerated during each phase. This analysis would typically utilize an energy balance equation like Equation (1).

(1) In Equation (1), g is the gravitational constant (32.2 feet/ sec2), fyaw, ftrip, froll are the deceleration rates during each phase of the crash, and dyaw, dtrip, droll are the distances over which each phase unfolds.

A number of studies have examined deceleration rates during the three phases. For example, for the roll phase, Arndt [2011] examined numerous published rollover tests – 81 dolly rollover tests, 24 naturally occurring rollovers (18 of which were steering-induced), and 102 reconstructed rollovers. For the dolly rollovers he compiled, Arndt found a mean roll phase deceleration rates of 0.44g with a standard deviation of 0.064g. For the naturally occurring rollovers, Arndt found a mean deceleration rates of 0.44g with a standard deviation of 0.063. Carter [2008] examined the accuracy of using a constant deceleration rate for the roll phase and concluded that vehicles’ deceleration rates were not constant over the roll distance and that they were higher during the early portions of the rolls than during the latter. Rose and Beauchamp [2009] explored the use of non-constant deceleration rates for the roll phase. Arndt [2012] applied a non-constant deceleration rate approach to the steering induced rollovers reported by Stevens [2011]. Cooperrider [1990, 1998], Larson [2000], and 12 Collision Magazine - Volume 11 Issue 1

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Asay [2009, 2010] all present data relevant to determining the deceleration rate and duration for the trip phase.

Carter [2012] examined methods for calculating the vehicle deceleration rate during the loss of control phase.

This article focuses on additional analysis of the deceleration during the trip phase of a soil-tripped rollover and highlights an issue that is missing from past studies of the trip phase for soil-tripped rollovers. During the trip phase, lateral forces applied to the tires or wheels on the leading side of the vehicle cause the trailing side tires to lose contact with the ground and the vehicle to begin tipping up.

Past discussions of the trip phase have focused solely on these lateral forces. However, in 2009 and 2010, Asay reported a series of steering-induced rollover crash tests that were conducted on a remote, rural highway in the west desert of Utah. The rollovers that resulted during these test series were soil tripped and, particularly for the 2010 tests, the instrumentation allows for examination of the forces applied to the vehicle during the trip phase.

Asay reported some limited analysis of the trip phase for the tests he published in 2010. He observed that, for all of the tests where rollover ultimately occurred, “the tripping phase was well defined by distinct furrows in the soil that terminated as the vehicle overturned.” He reported that the peak lateral acceleration experienced by the vehicles during the trip phase in these tests ranged from 2.76 to 5.70g. The peak lateral accelerations while furrowing through the dirt for the tests that did not ultimately result in rollover were 1.76 and 1.71g. Despite reporting these lateral acceleration values, Asay did not determine the vehicle deceleration rates during the trip phase, nor did he do any comparison of the deceleration rates to what past modeling efforts would predict. In our attempt to more fully analyze the trip phase of several of Asay’s 2010 tests, we have observed in the accelerometer data the presence of

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longitudinal accelerations applied to the vehicle during the trip phase. The brakes were not applied on the test vehicles during these tests until well after the roll had commenced, so these accelerations must be longitudinal accelerations that are generated from the tire interaction with the soil.

There are no prior studies that have examined the presence of longitudinal accelerations during the trip phase of soil-tripped rollovers. The purpose of this article is to initiate an exploration of such forces by examining one of Asay’s 2010 tests. Before doing that, though, consider how these longitudinal accelerations could be incorporated into analysis of the trip phase. rip Phase Deceleration Rate In 1990, Cooperrider reported a series of tests that included both curb tripped and soil tripped rollovers. Out of five attempts, two of the curb trip tests resulted in rollover. Both soil trip tests resulted in rollover.

The curb trip tests utilized a six inch (15 cm) square section of steel box tubing that was rigidly attached to the ground perpendicular to the vehicle’s travel direction. The test vehicles traveled laterally into the curb with their right sides leading at speeds around 30 mph (48 kph). In the two tests where rollover occurred as a result of the curb impact, the vehicles experienced average deceleration rates (lateral accelerations) during the trip of 12.4g and 13.2g.

These decelerations occurred over time periods of 60 milliseconds and 40 milliseconds. The soil tripped tests utilized a 40 foot by 20 foot (12.2 meter by 6.1 meter) area of loosely packed dirt and finely crushed granite. The test vehicles traveled laterally into this soil with their right sides leading. The initial speeds of the vehicles in these two tests were 33.7 and 27.0 mph (54.2 and 43.5 kph). The vehicle tires furrowed into the soil, causing the vehicles to rollover.

In these two tests, the vehicle’s experienced average deceleration rates during the trips of 1.62g and 1.71g. These decelerations occurred over time periods of 513 milliseconds and 460 milliseconds.

T

“

… we have observed in the accelerometer data the presence of longitudinal accelerations applied to the vehicle during the trip phase … these accelerations must be longitudinal accelerations that are generated from the tire interaction with the soil.

”

the sixth test. When the trip phase was defined as the time from when the vehicle entered the soil until the time a roll velocity of 100 degrees per second was reached, the average deceleration rate of the vehicles during the trip phase of both tests where rollover occurred was 1.23g (durations of 624 and 664 milliseconds). When the trip phase was defined as the time over which the vehicle reached a roll angle of 52 degrees, the average deceleration rates from the trip phase of the two tests were 1.05g and 1.39g (durations of 784 and 792 milliseconds). Neither of these definitions In 1998, Cooperrider reported six additional soil trip tests of the trip phase could be employed in practice, but the that each utilized a 1984 Oldsmobile Cutlass Ciera. The accelerations reported by Cooperrider are further illustrafirst test was run with an initial speed of 13.5 mph (21.7 tion of the magnitude of the lateral accelerations that can kph) and, as Cooperrider stated, “the subsequent test be associated with soil-tripped rollovers. speeds were established based on the previous test results Larson [2000] reported two soil-tripped rollover tests – in an effort to closely bracket the minimum trip speed.” one with a 1991 Jeep Wrangler and another with a 1986 Ultimately, the initial speeds for the vehicles in this test Chevrolet S-10 Blazer. Each vehicle was released without series ranged from 13.5 to 42.9 mph (21.7 to 69.0 kph). an initial roll angle and a slip angle of approximately 52 In the first four tests, the vehicle did not roll over. The degrees across a dirt surface with a 15.5 degree downslope. initial speed of the test vehicle in the fourth test was 21.2 The vehicles deposited tire and rim furrows in the dirt bemph (34.1 kph). In the fifth and sixth tests, which had fore beginning to roll over. Larson reported that the Jeep speeds of 23.0 mph and 42.9 mph (37.0 and 69.0 kph), Wrangler was traveling 40.7 mph (65.5 kph) when it was the vehicle rolled over – ½ roll in the fifth test and 1 roll in released from the dolly, that it traveled 70.5 feet (21.5 mewww.collisionpublishing.com

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ters) from release to rest, that it furrowed for approximately 20.7 feet (6.3 meters) before entering the roll phase, and that it completed one roll before coming to rest. The roll distance was approximately 49.8 feet (15.2 meters). Larson did not report a speed for the vehicle at the end of the furrowing. The average deceleration rate for the trip and roll phases combined was 0.78g. If we assume that the vehicle decelerated at a typical rate of 0.45g during the roll phase, then the deceleration rate during the trip phase was around 1.59g.

Larson reported that the Chevrolet S-10 Blazer was traveling 41.1 mph (66.1 kph) when it was released from the dolly, that it traveled 74 feet (22.6 meters) from release to rest, that it furrowed for approximately 18.1 feet (5.5 meters) before entering the roll phase, and that it completed two rolls before coming to rest. The roll distance was approximately 55.9 feet (17.1 meters). Again, Larson did not report a speed for the vehicle at the end of furrowing.

The average deceleration rate for the trip and roll phases combined was 0.76g. If we assume that the deceleration rate of this vehicle during the roll phase was around 0.45g, then the deceleration rate during the trip phase was around 1.73g.

occur over time periods on the order of 500 to 1000 milliseconds.

Cooperrider [1990] presented an equation for determining the lateral force necessary to trip a vehicle over a specified trip duration. Brach [2005] presented the following reworked form of Cooperrider’s equation, writing it in a more useable form that explicitly yields the normalized lateral tripping force in gravitational units. In this equation, m is the vehicle mass, kxx is the roll radius of gyration for the vehicle (Ixx=mkxx2, where Ixx is the roll moment of inertia), T is the track width, h is the center of gravity height, and ∆t is the trip duration.

(2)

This equation shows that the lateral accelerations during the trip depend on the trip duration. For long trip durations (1.5 seconds for instance), this equation will yield a value very close to the static stability factor. For shorter trip durations, however, higher lateral accelerations will be necessary to cause the trip over the prescribed duration.

In 2002, Hughes reported a series of 8 curb tripped roll- When reconstructing a particular rollover crash, the physiover tests conducted with 2001 Nissan Pathfinders. These cal evidence (i.e., the length of furrows) will determine the tests used a modified dolly fixture that did not introduce trip duration that should be input into Equation (2). As an initial roll angle to the test vehicles. Each vehicle was an example, consider the graph of Figure 2 which shows launched laterally from the dolly with a speed of around results from Equation (2) using the geometric and inertial 31 mph (50 kph) and then the leading side tires impacted parameters for the Isuzu Rodeo discussed in the next secthe curb, which was approximately 6 inches (15 centime- tion (SSF = 1.11). For this vehicle, the lateral deceleration ters) high. Rollover occurred in all 8 tests, with roll dis- during the trip phase begins approaching the SSF once the tances ranging from 54 to 72 feet (16.5 to 21.9 meters) trip duration exceeds 1 second. However, for shorter trip and the number of rolls varying between 1 and 2½ times. durations, the lateral deceleration is higher, particularly for Peak accelerations during the curb impacts ranged from trip durations of less than ¼ of a second.

14.5 to 20.5 g.

These tests demonstrate that the lateral accelerations present during the trip phase depend on the trip mechanism (i.e., soil versus curb). Any lateral acceleration that exceeds the vehicle’s roll threshold will result in a rollover if that lateral acceleration is sustained for long enough. The static stability factor (SSF), which is calculated by dividing the vehicle’s average track width by 2 times the center of gravity height, is one measure of this roll threshold. However, both curb trips and soil trips will typically be associated with lateral accelerations that exceed the static stability factor. The magnitude of these accelerations determines the trip duration. Curb trips are associated with lateral accelerations up above 10g and trip durations on the order of 50 to 100 milliseconds. Soil trips are typically associated with lateral accelerations between 1g and 3g, but soil trips 14 Collision Magazine - Volume 11 Issue 1

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Figure 2: Sample Results from Equation (2)

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Assume for now that the analyst could also estimate the magnitude of any longitudinal deceleration present during the trip phase. Then, the overall deceleration rate for the trip phase could be determined using Equation (3). In this equation, ftrip is the deceleration rate for the trip phase, α is the average vehicle slip angle during the trip phase (the angle between the vehicle heading and its velocity direction), and flong is any longitudinal acceleration that was applied to the vehicle during the trip phase from braking or furrowing effects. This equation accounts for the fact that only a portion of the lateral and longitudinal tripping forces would have been opposing the velocity and acting to decelerate the vehicle.

(3) est #2, SAE 2010-01-0521 (Isuzu Rodeo) This section examines the forces present during the trip phase of Test #2 from Asay’s 2010 publication.

This test utilized a 1991 Isuzu Rodeo and was conducted on the same remote, rural highway in the west desert of Utah on which nearly all of Asay’s tests were conducted. This highway was a relatively level, two lane roadway with a chip-seal coated surface and graded shoulders along both sides. In his 2010 study, Asay reported that the friction coefficient for this highway was 0.772.

The off-road terrain surrounding the road was open desert

T

Figure 3: Highway Where Asay’s Tests Were Conducted

with sagebrush and sparse grass. A photograph of this area is included in Figure 3.

In this test, the Isuzu was shifted into neutral and towed up to and released from a speed of 73.5 mph (118.3 kph).

After release, the vehicle was steered, using an automated steering controller, with a sharp left steering input of approximately ¼ turn that caused the vehicle to travel to the left across the roadway and to yaw counterclockwise. The vehicle developed sufficient slip that tire marks were deposited on the road surface. One second later, the leftward steering input was followed by a severe steering input back to the right. The vehicle continued off the left side of the road into the dirt, but reversed its yaw direction, developing a significant slip angle as it yawed in a clockwise manner. The vehicle deposited furrows in the dirt and then began rolling over. The vehicle rolled 7 times in 181 feet (55.3 meters).

Speed and position data was collected for the Isuzu using a VBOX III GPS sensor that was attached to the roof at the centerline and that collected data at a rate of 100Hz. A non-contact radar speed sensor was also attached to the rear bumper and accelerometers and rotation rate sensors were attached near the center of gravity. Asay surveyed the physical evidence both on and off the roadway from this test using a Leica total station and he provided his survey data to the authors. In this case, the physical evidence included tire marks, tire furrows, disturbed earth, wheel landings, glass spills, and debris from the vehicle. Based on Asay’s survey data, the evidence diagram of Figure 4 was produced.

Using the physical evidence diagram of Figure 4, it was determined that the total roll distance for this test was 181 feet. Asay [2010] reported a speed for the Rodeo at the beginning of the roll phase of 50.7 mph. Arndt [2011] reevaluated Asay’s analysis and reported a revised speed for the beginning of the roll phase of 44.4 mph. We have examined Arndt’s method of adjusting the speed and agree with his revised speed estimate. With a roll distance of 181 feet, a speed of 44.4 mph implies an average deceleration rate of 0.363g, outside of the normal range of rollover deceleration rates. Using a roll distance of 181 feet along with a deceleration rate of 0.44g for the roll phase – a more typical value – results in a calculated speed of 48.9 mph at

Figure 4: Evidence Diagram www.collisionpublishing.com

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the beginning of the roll phase, 4.5 mph higher than actual (44.4 mph).

Now, consider the trip phase of this test. Figure 5 contains nine frames from the video of this test that depict the final clockwise yaw motion of the vehicle that preceded the roll phase. Depending on how it is defined, the trip phase encompasses all or part of this final yaw. Based on the evidence diagram of Figure 4, five vehicle positions were determined for the clockwise yaw. Figure 6 depicts these positions along with four vehicle positions for the prior counter-clockwise yaw. The five positions associated with the final clockwise yaw motion of the vehicle are numbered in Figure 6. To determine the actual speed at each of these positions, the VBOX data was aligned with the positions in accordance with the alignment data reported

by Asay. Arndt’s corrected speed of 44.4 mph was used for the fifth position. However, for the first four positions of the final yaw, the vehicle had not developed significant roll angle or roll velocity, and so, correction of the speeds at these positions was not necessary. The process of aligning

the VBOX data resulted in the following speeds for the five positions identified in Figure 5 – 68.9 mph at the first position, 67.6 mph at the second position, 64.3 mph at the third position, and 61.1 mph at the fourth position.

One issue that arises in modeling the trip phase is deciding what portion of the final yaw constitutes the trip phase.

One approach would be to look at the tire mark evidence to determine when both trailing side tires have lifted off.

In this case, that occurred at Position 4. This approach has the advantages of being tied to the physical evidence and of using the theoretical definition of the trip phase (trailing wheel lift-off to leading wheel lift-off). That said, it is not clear that this is the best approach. First of all, in the development of this rollover – and of most real-world rollovers with a yaw that precedes the roll – the two trailing side tires lift-off at different points in time. In addition, the development of roll angle and roll velocity during the trip phase can start off slow and then accelerate quickly at the end. The tripping forces could be at work developing roll of the vehicle body while the trailing side tires remain in

Figure 5: Frames from the Video of the Trip Phase 16 Collision Magazine - Volume 11 Issue 1

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Figure 6: Loss of Control and Trip Phase Positions Determined from the Scene Evidence

Figure 7: Measurements for Loss of Control and Trip Phase Positions contact with the ground due to relaxing of the suspension.

Thus, choosing the position within the evidence where the second trailing side tire lifts off may result in the analyst missing a portion of the trip phase.

Another option for modeling the trip phase would be to use the position at which the first trailing side tire lifts off as the beginning of the trip phase. In this case, this occurs at the passenger side rear wheel around Position 3. This option suffers from the same potential problems as the first, though they would likely be less significant. Another option would be to treat the entire final yaw from Positions 1 through 5 as the trip phase. The calculations reported in this section considered each of these possible definitions of the trip phase. The segment distance and slip angle measurements used in these calculations are depicted in Figure 7 and the first set of calculations are summarized in Table 1. These calculations used Equations (1), (2) and (3), as-

sumed a deceleration rate during the roll phase of 0.363 to match the actual speed at the beginning of the roll phase, and used the following values for the Isuzu Rodeo.

Weight = 4,266 pounds Roll Moment of Inertia = 518 lb-ft-sec2 Radius of Gyration = 1.98 feet Track Width = 57.5 inches Center of Gravity Height = 25.9 inches The calculations summarized in Table 1 were initially run assuming there was no longitudinal tripping force present during the trip phase. Then, the longitudinal tripping force that would be necessary to match the actual deceleration rate during the trip phase was calculated.

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Trip Phase

Actual Trip Speed at Distance Beginning (feet) of Trip (mph)

Positions 61.1 4-5 Positions 64.3 3-5 Positions 68.9 1-5

Actual Deceleration Rate (g)

Calculated Lateral Deceleration (g)

Average Vehicle Slip Angle (deg)

Calculated Deceleration Rate Assuming No Longitudinal Acceleration

27.4

2.14

1.68

48

45.5

1.58

1.38

88.2

1.05

1.20

Implied Longitudinal Deceleration Rate (g)

1.23

Calculated Speed Assuming No Longitudinal Acceleration (mph) 54.6 (-6.5)

44

0.93

57.0 (-7.3)

0.88

28

0.57

58.9 (-10.0)

0.57

1.35

Table 1: Summary of Trip Phase Calculations (Assumes Roll Phase Deceleration Rate of 0.363g)

Several observations can be made based on the results reported in Table 1. First, with the assumption that there were no longitudinal accelerations present, Equations (2) and (3) underestimated the actual deceleration rate regardless of how the trip phase was defined. There was no braking applied to the vehicle during this test until after the vehicle began to roll, so whatever longitudinal forces were present must have been generated due to effects of furrowing. Including these forces would improve the accuracy of the speed analysis.

“

…with the assumption that there were no longitudinal accelerations present, Equations (2) and (3) underestimated the actual deceleration rate regardless of how the trip phase was defined.

”

a time of 0.5 seconds and continues until a time of 3.25 seconds. According to Asay’s article, time zero corresponds to release of the vehicle. The ground plane lateral accelerations shown in this graph were calculated from the measured vehicle frame lateral and vertical accelerations and the roll angle. The extents of the final yaw in Figure 8 were identified based on the roll velocity data from the test, the location of tire marks and furrows from the final yaw, and review of the video.

Figure 9 shows the relevant portions of the roll velocity data. As this graph shows, the roll velocity builds up during the trip phase over an approximately ½ second time frame from 2.5 to 3.0 seconds. The roll velocity then levels off to an approximately constant value of 400 degrees per second. This leveling off of the roll velocity is an indication that the trip phase has ended, the vehicle has become airborne, and the roll phase has begun. This plateauing of the roll velocity, which occurred at a time of approximately 3.005 seconds, was used to define the end of the trip phase.

This point is identified in Figure 9.

Next, the roll velocity data was integrated to obtain the roll angle of the vehicle body throughout the first 3.25 seconds of the test. Figure 10 shows these roll angles. The roll angle builds up over an approximate time frame of 2.5 to 3.0 seconds, achieving a roll angle of approximately 61 degrees at the end of the trip phase. Based on the determination that the trip phase ended 3.005 seconds after the vehicle was released, the video was reviewed and this time was identified. Figure 11 depicts a frame from the video showing the vehicle at the end of the trip phase. The video was Figure 8 shows the actual longitudinal and lateral (ground also reviewed in conjunction with the physical evidence plane) accelerations measured during the initial counter- diagram of Figure 5 to identify Positions 1 (beginning of clockwise yaw of the test vehicle and the subsequent clock- tire marks) and 4 (right front tire liftoff). These positions wise yaw that includes the trip phase. This graph begins at are shown in Figures 12 and 13. The video was not conducive to determining when the right rear tire lifted off.

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Figure 8: Longitudinal and Lateral Acceleration During Trip (Corrected for Roll)

Figure 9: Roll Velocity Data www.collisionpublishing.com

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Figure 10: Roll Angle Data Based on the timing of Positions 1, 4, and 5 in the video, these positions were identified on the graph of the ground plane (lateral) and longitudinal accelerations during the trip phase (Figure 14). Based on the timing of these positions, the average lateral (ground plane) and longitudinal accelerations experienced by the vehicle were determined for the time frame from Positions 1 to 5 and for the time frame from Positions 4 to 5. It was concluded that from Positions 1 through 5, the vehicle experienced an average lateral acceleration of 1.09g and a longitudinal accelera-

Figure 11: Frame from Video Showing Position 5 20 Collision Magazine - Volume 11 Issue 1

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tion of 0.47g. Both of these were acting in a direction that would decelerate the vehicle. During the time frame from Positions 4 to 5, the vehicle experienced an average lateral acceleration of 1.69g and a longitudinal acceleration of 0.64g.

These measured accelerations demonstrate that there were longitudinal forces applied to the vehicle during the trip due to furrowing effects. To these authors knowledge, there is no other literature that addresses this phenome-

Figure 12 : Frame from Video Showing Position 1

Figure 13: Frame from Video Showing Position 4

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Figure 14: Longitudinal and Lateral Accelerations During Trip with Key Times Labeled non of longitudinal forces arising due to furrowing during the trip phase of soil-tripped rollovers. Such longitudinal forces, therefore, have not customarily been included in analysis of the trip phase. Based on the analysis described in this section, however, their inclusion may be important to accurately calculating the vehicle speed at the beginning

of the trip phase. Using the actual longitudinal accelerations, the speed analysis scenarios reported in Table 1 were recalculated and are reported in Table 2. The scenario that used Positions 3-5 as the trip phase used a longitudinal deceleration of 0.55g.

Trip Phase Actual Trip Speed at Distance Beginning (feet) of Trip (mph)

Actual Calculated Deceleration Lateral Rate (g) Deceleration (g)

Average Slip Angle (deg)

Calculated Deceleration Rate Assuming Actual Longitudinal Acceleration

Calculated Speed Assuming Actual Longitudinal Acceleration (mph)

Positions 4-5 Positions 3-5 Positions 1-5

61.1

27.4

2.14

1.68

48

1.67

57.8 (-3.3)

64.3

45.5

1.58

1.38

44

1.35

61.7 (-2.6)

68.9

88.2

1.05

1.20

28

0.96

67.1 (-1.8)

Table 2: Summary of Trip Phase Calculations with Actual Longitudinal Accelerations (Assumes Roll Phase Deceleration Rate of 0.363g) www.collisionpublishing.com

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One issue with the calculations reported in Tables 1 and 2 is that they utilized a roll phase deceleration rate of 0.363g.

This was the deceleration rate that Arndt reported for this test, and so, using this value allowed for the trip phase calculations to be isolated. However, this roll phase deceleration rate is outside the range that would typically be employed by a reconstructionist. A more typical value would be 0.44g. Table 3 summarizes calculations similar to those reported in Table 2 (using the actual longitudinal decelerations), but this time using a roll phase deceleration rate of 0.44g. Using 0.44g results in overestimating the speed at the beginning of the roll phase, but with any definition of the trip phase, it improves the accuracy of the calculated speed over calculations that ignore the longitudinal forces.

Now, consider the loss of control phase or yaw phase.

Analysis of the yaw phase will be influenced by how one chooses to model the trip phase and any of the last five positions that are not included in the trip phase will become a part of the yaw phase. Speed calculations for the loss of

control were carried out based on the positions in Figure 5 for each of the trip phase definitions discussed above.

These calculations, which are summarized in Table 4 (roll phase deceleration rate of 0.363g) and Table 5 (roll phase deceleration rate of 0.44g), used the equations discussed by Carter [2012]. Figure 7 shows the distance and slip angle measurements used in these calculations. The slip angles for the front tires were not adjusted based on the steering inputs. Asay reported the vehicle was in neutral during this test, and so, no braking was applied to the vehicle during the loss of control. Based on the VBOX data, the actual speed at the beginning of the tire marks was 72.6 mph. iscussion and Conclusions The speeds reported in the second columns of Tables 4 and 5 assume that there were no longitudinal accelerations present during the trip phase. Thus, these speeds carry the error from the trip phase into the analysis of the yaw phase. The results in the third columns of Tables 4 and 5 assume the actual longitudinal accelera-

D

Trip Phase Actual Trip Speed at Distance Beginning (feet) of Trip (mph)

Actual Calculated Deceleration Lateral Rate (g) Deceleration (g)

Average Slip Angle (deg)

Calculated Deceleration Rate Assuming Actual Longitudinal Acceleration

Calculated Speed Assuming Actual Longitudinal Acceleration (mph)

Positions 4-5 Positions 3-5 Positions 1-5

61.1

27.4

2.14

1.68

48

1.73

61.8 (+0.7)

64.3

45.5

1.58

1.38

44

1.37

65.3 (+1.0)

68.9

88.2

1.05

1.20

28

0.96

70.3 (+1.4)

Table 3: Summary of Trip Phase Calculations with Actual Longitudinal Accelerations (Assumes Roll Phase Deceleration Rate of 0.44g)

Trip Phase

Positions 4-5 Positions 3-5 Positions 1-5

Calculated Speed at Beginning of Tire Marks (mph), Assuming No Longitudinal Deceleration 61.5 (-11.1) 62.1 (-10.5) 61.9 (-10.7)

Calculated Speed at Beginning of Tire Marks (mph), Using Actual Longitudinal Deceleration 64.3 (-8.3) 66.4 (-6.2) 69.7 (-2.9)

Table 4: Summary of Loss-of-Control Phase Calculations

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Trip Phase

Positions 4-5 Positions 3-5 Positions 1-5

Calculated Speed at Beginning of Tire Marks (mph), Assuming No Longitudinal Deceleration 65.2 (-7.4) 65.7 (-6.9) 65.4 (-7.2)

Calculated Speed at Beginning of Tire Marks (mph), Using Actual Longitudinal Deceleration 67.9 (-4.7) 69.8 (-2.8) 72.8 (+0.2)

Table 5: Summary of Loss-of-Control Phase Calculations

(Assumes Roll Phase Deceleration Rate of 0.44g)

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tion for each trip phase duration. The results of Tables 4 and 5 suggest, first, that it is important to include the longitudinal accelerations during the trip phase to calculate the vehicle speed at the beginning of the loss of control.

However, a conservative speed estimate could be obtained without including these accelerations. Finally, these calculations suggest that defining the trip phase as the entire last yaw leading into the roll may be the most accurate. This hypothesis could be tested by analyzing and reconstructing the additional rollovers reported by Asay in 2010. These authors intend to publish additional parts of this study reporting such analysis.

Developing a reliable method for incorporating longitudinal accelerations into calculations related to the trip phase obviously requires more than analysis of one test. In addition to analyzing the other rollover tests reported by Asay, it would be worthwhile to explore analytical models for calculating the longitudinal force from tire to soil interaction. eferences 1. Arndt, Mark, “Drag Factors from Rollover Crash Testing for Crash Reconstructions,” Proceedings of the ASME 2011 International Mechanical Engineering Congress & Exposition IMECE2011 November 11-17, 2011, Denver, Colorado, IMECE2011-65537.

R

2. Arndt, M., Stevens, D., and Arndt, S., “Comparison of Linear Variable Deceleration Rate Rollover Reconstruction to Steer-Induced Rollover Tests,” SAE Technical Paper 2012-01-0469, 2012, doi:10.4271/201201-0469.

3. Asay, A. and Woolley, R., “Rollover Testing on an Actual Highway,” SAE Int. J. Passeng. Cars – Mech. Syst.

2(1):1237-1267, 2009, doi:10.4271/2009-01-1544.

4. Asay, A. and Woolley, R., “Rollover Testing of Sport Utility Vehicles (SUVs) on an Actual Highway,” SAE Technical Paper 2010-01-0521, 2010, doi:10.4271/2010-01-0521.

5. Brach, Raymond M., Brach, R. Matthew, Vehicle Accident Analysis and Reconstruction Methods, SAE, 2005.

6. Carter, J., Luepke, P., Henry, K., Germane, G. et al., “Rollover Dynamics: An Exploration of the Fundamentals,” SAE Int. J. Passeng. Cars - Mech. Syst.

1(1):80-104, 2008, doi:10.4271/2008-01-0172.

7. Carter, N., Beauchamp, G., and Rose, N., “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.

8. Cooperrider, N., Thomas,T., and Hammoud, S., “Testing and Analysis of Vehicle Rollover Behavior,” SAE Technical Paper 900366, 1990, doi:10.4271/900366.

9. Cooperrider, N., Hammoud, S., and Colwell, J., “Characteristics of Soil-Tripped Rollovers,” SAE Technical Paper 980022, 1998, doi:10.4271/980022.

10. Hughes, R., Lewis, L., Hare, B., Ishikawa, Y. et al., “A Dynamic Test Procedure for Evaluation of Tripped Rollover Crashes,” SAE Technical Paper 2002-010693, 2002, doi:10.4271/2002-01-0693.

11. Jones, I., “The Mechanics of Rollover as the Result of Curb Impact,” SAE Technical Paper 750461, 1975, doi:10.4271/750461.

12. Larson, R., Smith, J., Werner, S., and Fowler, G., “Vehicle Rollover Testing, Methodologies in Recreating Rollover Collisions,” SAE Technical Paper 2000-011641, 2000, doi:10.4271/2000-01-1641.

13. Martinez, J. and Schlueter, R., “A Primer on the Reconstruction and Presentation of Rollover Accidents,” SAE Technical Paper 960647, 1996, doi:10.4271/960647.

14. Meyer, S., Davis, M., Forrest, S., Chng, D. et al., “Accident Reconstruction of Rollovers - A Methodology,” SAE Technical Paper 2000-01-0853, 2000, doi:10.4271/2000-01-0853.

15. Orlowski, K., Moffatt, E., Bundorf, R., and Holcomb, M., “Reconstruction of Rollover Collisions,” SAE Technical Paper 890857, 1989, doi:10.4271/890857.

16. Rose, N. and Beauchamp, G., “Development of a Variable Deceleration Rate Approach to Rollover Crash Reconstruction,” SAE Int. J. Passeng. Cars – Mech. Syst. 2(1):308-332, 2009, doi:10.4271/200901-0093.

17. Stevens, D., Arndt, S., Wayne, L., Arndt, M. et al., “Rollover Crash Test Results: Steer-Induced Rollovers,” SAE Technical Paper 2011-01-1114, 2011, doi:10.4271/2011-01-1114.

18. Wilson, Lawrence A., et al., “Reconstruction and Analysis of Steering-Induced, On-Road, Untripped SUV Rollover Tests (Part 1),” Collision – The International Compendium for Crash Research, Volume 2, Issue 1, Spring 2007.

19. Wilson, Lawrence A., et al., “Reconstruction and Analysis of Steering-Induced, On-Road, Untripped SUV Rollover Tests (Part 2),” Collision – The International Compendium for Crash Research, Volume 2, Issue 2, Fall 2007.

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NEXT-GENERATION CRIME AND CRASH SCENE MAPPING TECHNOLOGY IS ALREADY HERE Law enforcement agencies face a unique challenge when it comes to investigating crash and crime scenes. Above all, the information investigators collect must be thorough and accurate, but these situations rarely cater to the needs of the investigator. Crash and crime scene personnel often must accurately log data near busy roads and major intersections, battle adverse weather conditions, and navigate extremely tight quarters or far-flung spaces across varying landscapes. They also must map these sites as quickly as possible so additional teams can work the scene and collect evidence for their own reports and investigations.

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Modern 3D scanners enable investigators to accurately record millions of points of the scene. Just like total stations, using 3D scanners takes time to learn and master and require multiple setups to effectively capture all the details of the scene. When the scanner is moved around, the “point clouds” need to be merged together using expensive office software. This can take hours of work in the office before a 3D model can be rendered. Many users are overwhelmed with all the data and often would prefer a simpler mapping solution. Also, depending upon the model, highend 3D scanners can cost over one-hundred thousand dollars, making it unattainable for smaller departments.

THE MODERN SOLUTION The advent of the smartphone, tablet, and other mobile technologies gave rise to a generation of smarter mapping tools, and crash and crime scene investigators have broader access to affordable, fieldto-office solutions that streamline the data collection and mapping process.

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LTI’s Incident Mapping Kit includes the TruPoint 300™ laser measurement tool and the QuickMap 3D® for Android software, enabling investigators to map only the evidence they need. This easy-to-use, combined technology facilitates: • Taking photos of the scene with the laser or tablet and have them automatically matched up with your data set • Instant processing and real-time diagram review to ensure accuracy and completeness before leaving the scene • Wireless information transfer, eliminating cables and connectivity issues by simply emailing field data right from your tablet QuickMap 3D has the ability to collect and store data and photos and export them all in a CAD-friendly format so investigators aren’t re-creating drawings from handwritten notes. LTI’s kit also includes IMS Map360, MicroSurvey’s CAD-based forensic diagramming software. Users can access commonly used incident reconstruction formulas, choose between the easy-to-use sketch or advanced mode, and have the ability to see the diagram with interactive templates before printing.

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dy

tu e S

Cas

Crash Data Case Problems

w. R. Rusty Haight

Collision Safety Institute

F

or this issue of Collision, rather than work on a single crash and associated information as a case problem, the theme is “focused data interpretation” where using more than CDR Tool report data sets would make for a more complete analysis.

What follows are examples of data from CDR Tool generated reports and then narrowly focused questions associated with that data. The goal is to present select data elements which might otherwise be overlooked or perhaps under appreciated in a review of crash data and then examine those elements and how they might be better understood or applied together with an analysis of the available or necessary human, vehicle and environmental information we might need to fully answer the questions.

In the examples, one may assume the data is presented as it would appear in a report generated with the latest version of the CDR Tool software. While it would normally not be appropriate to “work in a vacuum” - not reviewing all the elements in a report during a more complete analysis in a given case - the narrow scope of the element(s) to be examined in the context of these case problems may be done with the information available or provided...unless otherwise indicated.

Project: “2005 Pontiac G6” The data you may review for this example comes from a 2005 Pontiac G6 as seen in Figures 1-6. You may assume that the vehicle has been examined and there were no airbags or belt pretensioners deployed.

There is some visible damage to the front and some to the rear of the Pontiac. You find another involved car at scene in front of the Pontiac with some visible damage to the rear of that car. A third car, behind the subject Pontiac, has some minor damage to its front. To move this problem forward, we will agree that there are no other cars involved, there is no other related damage to the involved vehicles and we’ll adopt that the data we’re working with comes from the crash at hand. The issues you have to resolve for this data evaluation example are: 26 26 Collision Collision Magazine Magazine - Volume - Volume 11 Issue 11 Issue 1 1

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1. The data for the recorded Non-Deployment reports a value for “Maximum SDM Recorded Velocity Change (MPH).” There are also data tables for “SDM Longitudinal Axis Recorded Velocity Change (MPH).” and “SDM Lateral Axis Recorded Velocity Change (MPH)” which report the delta-V relative to those axes. Start by comparing the delta-V from the data tables, of course in the context of the Data Limitations, with that reported as the “Maximum SDM Recorded Velocity Change (MPH).” Are there any apparent “differences?” If so, how might that be resolved?

2. There is one recorded event; the Non-Deployment event. Based on the remainder of the data found in the report from the Pontiac, this module “saw” a total of how many related events?

3. Based on the data for the Pontiac as recorded and reported, there was an “observed” but not recorded event which would have had a delta-V of greater than _______ but less than _________ (select units as appropriate) and would have occurred within ______ seconds of the recorded event. How would this other event relate to the overall crash sequence as described?

4. How is the pre-crash data found in the CDR Tool report from the Pontiac associated with the recorded Non-Deployment?

5. Relative to the pre-crash data for the Pontiac, we find a set of data “Pre-Crash” elements marked as relative to time “-1sec” (Figure 4). That set of data would have been captured by the ACM approximately how long (in time) before the ACM began it’s first decision making algorithm in this series?

6. Relative to the pre-crash data for the Pontiac, we find a set of data “Pre-Crash” elements marked as relative to time “-1sec” (Figure 4). That set of data would have been captured by the ACM approximately how long (in time) before the ACM began it’s decision making algorithm for the recorded event’s data set?

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Figure 1: CDR File Information for 2005 Pontiac G6

Figure 2: Data Limitations for 2005 Pontiac G6 www.collisionpublishing.com

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Figure 3: Multiple Event Data for 2005 Pontiac G6

Figure 4: Pre-Crash Data for 2005 Pontiac G6

Figure 5: System Status at Non-Deployment for 2005 Pontiac G6

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Figure 6: Longitudinal and Lateral Delta-V Data 2005 Pontiac G6 Project “2010 Ford F150 Raptor” The data for this case problem comes from a 2010 Ford F150 Raptor. Prior to the crash, the Raptor was approaching a four way intersection (imagine two essentially intersecting perpendicular roads) and the Raptor driver claims he was planning to make a turn at the intersection. A smaller passenger car was approaching the intersection and that driver claims he was not planning to turn but continue straight through. The passenger car struck the Raptor at the right rear wheel well and tire as seen in Figure 7 ripping the right rear wheel and tire off the truck. We may adopt, for the case problem, that there is resulting damage on the front chiefly at the right front of the passenger car and that the only damage to the Raptor is at the right rear wheel/wheel well. The issues you have to resolve for this data evaluation example case problem are:

2. Assuming that immediately prior to the pre-crash data period the Raptor was traveling along a straight path, call it a baseline for this analysis. At impact, how many degrees had the Raptor turned from that path and in what direction (i.e.: right or left relative to the driver’s perspective in the truck)?

3. Based on the data available for the Raptor, what would be the approximate PDOF for the truck (what angle relative to what vehicle axis)?

4. Given the driving pattern established thus far for the Raptor, can you determine if the smaller passenger car had been originally approaching from the opposite direction the truck had been traveling or on the cross street prior to impact?

1. Based on the data retrieved from the Raptor as offered in the sections of the CDR Tool report found here as Figures 8-13 and assuming that prior to the pre-crash period found in the data the truck was moving in essentially a straight line toward the intersection, did the driver appear to be turning left or right from his original path into the intended turn?

1.a Would the data from the Raptor support the driver making or appearing to make a U-turn (assume on to be legal at this intersection)?

Figure 7: Photo of 2010 Ford F150 Raptor www.collisionpublishing.com www.collisionpublishing.com

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Figure 8: CDR File Information for 2010 Ford F150 Raptor

Figure 9: Data Limitations for 2010 Ford F150 Raptor

Figure 10: Deployment Data for 2010 Ford F150 Raptor 30 30 Collision Collision Magazine Magazine - Volume - Volume 11 Issue 11 Issue 1 1

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Figure 11: Pre-Crash Data for 2010 Ford F150 Raptor

Figure 12 (above): Pre-Crash Data for 2010 Ford F150 Raptor Figure 13 (right): Longitudinal and Lateral Crash Pulse Data for 2010 Ford F150 Raptor See The Case Problem Solutions on page 98 www.collisionpublishing.com www.collisionpublishing.com

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s c i t a m e n i K t n a p u c d c n O E e r l a a e m R e F w-Speed Lissette M.oRxu, Jbre.ratnéd, o S. C n Lantz y L l l i B n i isions Susa Coll 34 Collision Magazine - Volume 11 Issue 1

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A

bstract

Traditionally, women and older adults have been underrepresented as test subjects in full-vehicle crash tests.

Some studies have reported an increased incidence and duration of whiplash-associated disorders (WAD) for females compared to males. Females are generally thought to be at greater risk of experiencing initial symptoms of WAD at lower impact severity collisions than males. They have also been reported to have twice the risk of experiencing symptoms lasting longer than one month and twice the risk of injuries leading to permanent impairment compared to men. Older adults are often thought to have pre-existing conditions and other natural aging processes that may increase the risk of injury compared to younger adults under similar crash conditions.

In low-speed vehicle collisions, occupants experience changes in velocity and acceleration levels that are typically low in magnitude. In this article, we present a review of the literature, as well as, new female occupant crash test data. This data adds to the body of literature regarding occupant kinematic response during collinear low-speed rear-end impact collisions.

A series of eight low-speed vehicle-into-barrier impacts tests were conducted with two instrumented female volunteers aged 35 and 60 years. The volunteers, both authors of this paper and developers of the paper’s title, had similar gross anthropometry, representative of the mean body height and weight of the U.S. female population. The older lady was exposed to three consecutive rear-end collisions resulting in a change in velocity of up to 2.0 m/s (4.5 mph) while unrestrained.

The younger lady was exposed to five rearend collisions with changes in velocity of up to 2.6 m/s (5.8 mph). The younger lady was unrestrained in all but one test. Range of motion and orthopedic tests were performed by a chiropractor within hours before and three days after the subject test. No clinically significant differences were noted in the posttest evaluation of either subject. Neither subject complained of pain or injury at the three-day post-test follow-up examination.

L

Injury Risk, Severity and Symptom Duration. The most widely accepted parameter for defining the severity of a collision is the Delta-V or change in velocity experienced by a vehicle. Delta-V has been traditionally used as a predictor for occupant injury in vehicular collisions. Roberts, et al.

(1993) compiled data from 20,000 collisions of passenger cars, light trucks and utility vehicles from the data contained in the National Accident Sampling Study (NASS) database.

The authors observed, “The National Crash Severity Study (NCSS) by relating accident severity, expressed in terms of Delta V, with injury severity to the form of the Abbreviated Injury Scale (AIS) has provided an (sic) convenient means to start to quantify the relationships with may exist between injuries, their severity and the change in velocity in a collision.” AIS is an anatomically based scoring system that classifies individual injuries by body region on a scale of 1 to 6 (1 = minor and 6 = maximum). In low-speed rearend impact collisions, the majority of the symptoms or injuries claimed are classified as minor (AIS 1), and are non-life threatening.

The authors concluded that the incidence of soft tissue injuries are shown to be related to the change in velocity in a collision.

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Injury claims are disproportionately large in view of the minor vehicle structural damage. In many instances police reports are not filed and vehicles are driven from the scene. The reporting period for the onset of symptoms varies greatly among occupants. In some cases, it may be immediate and only last a few hours. In other cases, the initial onset of symptoms are not reported until weeks or months following the collision. In some cases, these minor injuries are associated with long term symptoms (greater than 1 year) and permanent impairment. Given the high incidence of injury claims, numerous research studies have been conducted to better understand the forces imposed on the occupant during low-speed rear-end impact collisions.

Over the years, full-vehicle crash tests and sled tests have been conducted with instrumented human volunteers and anthropomorphic test dummies. Several criteria have been proposed as predictors of neck injury potential. The Neck Injury Criterion (NIC) is the most widely accepted parameter for evaluating neck loading during the retraction phase of the occupant motion. Boström et al. (1996) proposed a preliminary human threshold of NIC < 15 m2/s2. According to risk curves developed by Kullgren et al. (2003), 99% ± 2% of individuals who experience a NICmax of less than 15.3 m2/s2 will not experience neck symptoms for longer than 1 month.

The research literature contains numerous parameters associated with an increased risk for injury, which can generally be classified into three distinct categories: 1) crash related parameters, 2) seat properties and 3) occupant factors. Crash related parameters include, the Delta-V, and the peak and mean vehicle acceleration. Seat properties include the geometric and dynamic properties of the seat.

Occupants factors include, the age, gender, and body posture of the occupant. Each of these categories will be describe in more detail below.

Crash Related Parameters It is generally acknowledged that the mass of the vehicles, as well as the bumper geometry and stiffness properties can influence the magnitude and duration of the crash pulse and consequently the kinematic response of the occupant (Linder et al., 2003; Siegmund et al., 2005; Szabo et al., 2002). Both Delta-V and mean acceleration have been shown to influence the risk of AIS 1 neck injuries (Krafft et al., 2002; Krafft et al., 2005). Krafft et al. (2005) studied 150 real world rear-end accidents using data from seven different vehicle models equipped with a crash pulse recorder (CPR) with a trigger level of approximately 3g. The authors reported a 20% risk of symptoms lasting longer than a month for occupants with no prior long-term AIS 36 Collision Magazine - Volume 11 Issue 1

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1 neck injury that had been exposed to a Delta-V of 5 m/s (11.18 m/h) and mean vehicle acceleration of 5 g. The risk data indicated above is reflective of real-world accidents, and is independent of vehicle seat properties and occupant factors such as body position, head turn, and pre-existing conditions. Furthermore, many of the occupants included in the study were tasked with self-reporting their injuries, thus rendering the diagnosis of AIS 1 neck injuries difficult to objectively determine (Krafft et al., 2002; Krafft et al., 2005). Indeed, the authors indicate that “Better significance could be expected if only symptoms verified by a medical doctor were used” (Krafft et al., 2005). However, the clinical diagnosis of whiplash associated disorders (WAD) after a traffic collision is confounded by the high prevalence of neck pain and WAD-like symptoms in the general population (Hogg-Johnson et al., 2009). Thus, the rate of medically confirmed injury may be lower than those reported.

Crash sled tests used to evaluate the safety performance of seats/head restraints define the standard Delta-V and crash pulse representative of the conditions where the majority of rear impacts are reported. The Insurance Institute for Highway Safety (IIHS) simulates a moderate rear-end impact with a velocity change of 4.1 to 4.5 m/s (9.2 to 10.1 mph) using a 88 to 94 msec acceleration pulse with a 9.5 to 10.5 g peak and 4.4 to 5.2 g mean acceleration (RCARIIWPG Seat/Head Restraint Evaluation Protocol, version 3). The European New Car Assessment Programme (Euro NCAP) uses three different acceleration pulse requirements, representative of a low, moderate and high speed rear-end accident. The low severity sled pulse simulates a rear-end impact with a velocity change of 4.5 ± 0.2 m/s (10.1 ± 0.4 mph) and 5.0 ± 0.5g peak acceleration and 4.3 ± 0.5 g mean acceleration (Euro NCAP The Dynamic Assessment of Car Seats for Neck Injury Protection Testing Protocol, version 3.1). Real world data indicates that 41% of reported single rear-end collisions do not reach the CPR trigger level of 3 g (Krafft et al., 2005).

Seat Properties Different automobile seats have different padding/geometry/stiffness and energy absorbing features which may lead to differences in the kinematic response of the occupant.

These differences are evident even within the same vehicle when comparing the front row to the rear row seats. Regardless of the severity of the collision or the direction of impact, drivers and front seat passengers have been reported to sustain WAD more often than rear seat passengers (Otremski et al., 1989). This may simply be due to the fact that front seats are much more likely to be occupied than rear seats.

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Seat geometry is mandated by federal regulation. Earlier safety standards aimed at preventing hyperextension injuries of the head/neck via the implementation of head restraints. In March 2008, a Global Technical Regulation on head restraints was adopted with the objective of minimizing rapid head and neck movement relative to the torso. The regulation recommended positioning the head restraint closer to the occupant’s head (reduced backset) and increasing the height of the head restraint. Recent revisions to the federal motor vehicle head restraint safety standard (FMVSS 202a) requires a minimum head restraint height of 750 mm above the hip and limits the backset to 55 mm behind the head of a dummy representing a 50th percentile male occupant (Docket No. NHTSA–2007–27986). FMVSS 202a applies to passenger vehicles built after September 1, 2009. The phase in requires all front seats to comply with the new regulation

by September 1, 2010 and all rear seats by September 1, 2011. The revised requirements of FMVSS 202 will put all head restraints in either the good or acceptable safety rating based on the IIHS geometric rating criteria (Docket No. NHTSA–2007–27986).

Some manufacturers have implemented automotive seat design enhancements which include improved geometry and dynamic properties, i.e. active head restraints and/or energy absorbing seatbacks. It is important to note that in many low-velocity collisions the severity of the impact is not of sufficient magnitude to activate the whiplash protection concepts. Volvo, for example, introduced the Whiplash Injury Protection System (WHIPS). It is designed to minimize occupant accelerations by allowing the seatback to move rearwards in a controlled manner (Lundell et al., 1998). The recliner has been reported to activate in changes of velocity greater than 2.8 m/s (6.3 mph) (Lundell et al., 1998). Kullgren and Krafft (2010) reported that vehicles fitted with advanced whiplash protection concepts had approximately 50% lower risk of injury leading to permanent impairment compared to standard seats.

Davidsson and Kullgren (2013) reported a reduction in risk of injuries leading to permanent impairment between 27 to 59%.

Seat padding appears to be less influential on the occupant kinematics. Szabo et al. (2003) studied the influence of the seating foams (i.e., standard, viscoelastic, low or high density) on occupant dynamics using a BioRID II test dummy subjected to rear impact tests with a nominal Delta-V of 3.0 m/s (6.7 mph). The authors reported that the head restraint geometry were more influential on occupant kinematics and injury potential than the local foam properties within a given seat.

Some studies have attempted to establish a relationship between seat rating and risk of neck injury in real-world accidents. Kullgren et al. (2007) reported that cars rated in the worst group by the IIHS and Euro NCAP had 43% higher risk of long term symptoms in real-life crashes, than cars rated in the best group. Farmer et al. (2008) studied claims data from two large automobile insurers for passenger cars and SUVs involved in a rear-end collision.

When comparing driver neck injury rates by vehicle damage severity and IIHS seat rating, no significant difference was found in percent of drivers claiming neck injuries for vehicles with minor damage severity. Vehicles with seats rated as good, acceptable, marginal and poor had 11.1%, 15.7%, 12.1% and 13.4 % of driver neck injury claims, respectively.

Occupant Factors Age NHTSA recently reported that one of the most important factors that affects a person’s risk of injury in a motor vehicle crash is the age of the occupant (Lui et al., 2007).

In 2012, 14 percent of the total U.S. resident population (43.1 million people) were 65 and older (NHTSA, 2014).

Based on the data pertaining only to drivers from NHTSA’s National Automotive Sampling System — Crashworthiness Data System (NASS-CDS) from 1993 through 2004, the body region with the highest percentage of minor to serious injury (MAIS ≤ 3) in rear-impact crashes for older drivers (age ≥65) is the lower limbs, while for younger and middle aged adults is the neck and spine. It is important to note that NASS-CDS contains data on passenger vehicle crashes in which at least one vehicle was towed due

“

NHTSA recently reported that one of the most important factors that affects a person’s risk of injury in a motor vehicle crash is the age of the occupant

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to damage, which is usually not the case in a low-speed rear-end collision. Data obtained from insurance claim reports in which at least one occupant was injured document a moderately increased risk of whiplash injury for the younger (18-54) age groups when compared with the oldest (55-65) age group (Berglund et al., 2003).

A common pre-existing condition observed in the spine of older adults is disc degeneration. It is estimated that 60 to 80% of the adult population has at least mild degeneration in the lumbar spine by the age of 50. By the age of 60, 70% of women have at least one degenerated cervical disc (Miller et al., 1988; Boden, 1996; Gore et al. 1986). No scientifically valid studies examining whether degenerative changes in the cervical spine increase the risk of WAD after a traffic collision have been published (Holm et al., 2009). However, the Bone and Joint Decade 2000-2010 Task Force on Neck Pain and its Associated Disorders has argued against an association between disc degeneration and injury risk given that younger age groups are more likely to seek health care or file a claim for WAD (Holm et al., 2009).

Although crash testing with human volunteers above the age of 55 years is not common, studies do not support an increased risk of WAD for older adults. Braun et al.

(2001) tested two male subjects aged 56 and 61 years, McConnell, et al. (1993) tested one 56 year old male subject, McConnell, et al. (1995) tested one 59-year-old male subReference Brault et al. (1998) Braun et al. (2001) Castro et al. (1997) Fugger et al. (2003) Furbish et al. (2011) Ivory et al. (2010) Keifer et al. (2005) Mills et al. (2004) Szabo and Welcher (1996) Szabo et al. (1994) Welcher and Szabo (2001) Welcher et al. (2001a) Welcher et al. (2001b) Cox et al unpublished (1997) Cox et al unpublished (1998) Total

Female Subjects 21 1 5 5 1 1 1 3 1 2 1 1 2 2 1 48

ject, and Szabo et al. (1994) tested one 58-year-old female subject. Some of the above volunteers, including those exposed to multiple impacts, reported only minor transient symptoms lasting less than four days. In previously unpublished work, Cox, et al. (1999) performed a series of three, front-to-rear collisions with one 68-year-old male subject. The volunteer in this series reported no symptoms immediately following the test or the following day.

Gender The research literature is inconsistent regarding an associated between gender and risk of WAD. Using data from real-world accidents, Kraft et al. (2005) reported that females have a greater risk of sustaining initial symptoms at lower impact severity than males. In a more comprehensive study which included data from three different data sets, Kullgren and Krafft (2010) reported that women have 50% higher risk of reporting an injury in rear-end crashes than males and they have twice the risk of symptoms lasting longer than one month and twice the risk of injuries leading to permanent impairment compared to men. The authors further reported that whiplash protection concepts are more effective in reducing the risk of injuries for males than females (Kullgren and Krafft, 2010).

Temming (1998) reported that the risk of neck injury to belted female occupants during a rear end collision is 1.9 to 2.4 times greater than for belted male occupants. The authors note that injury risk reported may be high because Age Range 20-40 31 26-37 21-26 42 41 41 18-42 28 27, 58 32 33 32, 41 26, 32 26 18-58

∆V Range (m/s) 1.1-2.2 1.2-1.8 2.4-3.9 0.9-2.7 2.4-2.8 2.0-2.5 2.13 ± 0.05 0.9-3.1 4.0-4.2 2.2 1.1-2.2 0.5-2.3 0.2-2.2 0.93-2.90 1.34 0.2-4.2

∆V Range (mph) 2.46-4.92 2.68-4.02 5.36-8.72 2.01-6.03 5.36-6.26 4.47-5.59 4.76 ± .11 2.01-6.93 8.94-9.39 4.92 2.46-4.92 1.11-5.14 0.44-4.92 2.1-6.5 3 0.44-9.39

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the database only includes accidents in which at least one person was injured. Additionally, accidents were not classified by severity or Delta-V. Siegmund et al. (1997) initially reported greater and earlier peak head acceleration for females compared to males. However, a later more comprehensive analysis found that the previously observed gender-dependent findings were in fact likely not correlated to gender, but instead could be explained by relative head restraint position (Siegmund et al., 1999). A number of hypotheses have been suggested to explain differences in the injury risk between men and women which point either to differences in behavior patterns, anthropometry, and/or the anatomical properties of the head/neck complex. Examples of such hypotheses include, women weigh less than men, are shorter, have smaller and longer necks, have lower upper torso mass distribution and sit closer to the steering wheel and dashboard compared to men (Carlsson et al., 2011; Carlsson et al., 2012).

Crash testing with female volunteers is scarce. A summary of low-velocity full-vehicle rear impact tests conducted with female volunteers is summarized in Table 1. Szabo et al. (1994) tested a 58-year-old female with minor cervical disc degeneration and moderate cervical disc bulge or protrusion. The female subject was exposed to two impacts with a Delta-V of 2.2 m/s (4.9 mph) and a maximum head acceleration of 13.7 g. Immediately post-impact the volunteer reported a transient headache which resolved spontaneously prior to exiting the vehicle. No other symptoms were reported in the one-year period following the tests.

Body Posture Research suggests that occupant’s seating posture relative to the head restraint may play a factor in the development of WAD. Current head restraint geometry evaluation ratings by the IIHS include static measurements of headrest height relative to the top of the head and backset. The term backset refers to the horizontal distance between the back of the head and the front surface of the headrest. A decrease in backset is associated with decreased rearward head displacement, cervical extension and decreased head accelerations (Szabo and Welcher, 1996). A head restraint position that is higher and closer to the head may lead to a reduced risk of cervical spine injury (Kleinberger et al., 1999). Thus, federal regulation mandates a minimum head restraint height and limits the backset distance. Additionally, some manufacturers have implemented active head restraints that move upward and forward resulting in improved dynamic head support regardless of manual head restraint adjustment (Viano and Olsen, 2001).

Seat belt use has only a minor effect during low-speed rearend collisions. In some cases, seat belt use may minimize

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A series of eight vehicle-intobarrier tests were conducted with two instrumented female volunteers aged 35 and 60 years.

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ethods A series of eight vehicle-into-barrier tests were conducted with two instrumented female volunteers aged 35 and 60 years. The older female volunteer was exposed to three consecutive rear-end collisions, resulting in a change in velocity of up to 2.0 m/s (4.5 mph), while the younger female volunteer was exposed to five rear-end collisions with a change in velocity of up to 2.6 m/s (5.8 mph). The tests were conducted on a single day at the Las Vegas Motor Speedway.

Volunteers Table 2 lists anthropometric data for both subjects. The height and weight of both volunteers approached the mean body weight and height of the U.S. female population within their respective age groups based on the National Health and Nutritional Examination Survey (Ogden, 2004). Head acceleration was measured using a nine-channel head array consisting of three tri-axial accelerometers (ICS 3031-050 ± 50 g) mounted on a lightweight headband. The headband was made of rubber that formed a secure bond when it was pulled tight and fas-

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ramping and thus the likelihood of the head rotating over the headrest for taller occupants. McConnell et al. (1995) reported no apparent significant qualitative or quantitative differences in the kinematic responses of unrestrained when compared to restrained occupant tests for Delta-Vs up to 3.0 m/s (6.7 mph). Nielsen et al. (1997) reported that the seat belt began to arrest the forward motion of test subjects when the collision severity approached a Delta-V of 1.4 to 1.9 m/s (3.1 to 4.3 mph). Liu et al. (2007) reported no significant safety effect of driver seat belt use for any age group in rear impact crashes based on real world data from NHTSA’s NASS-CDS from 1993 to 2004.

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tened around the driver’s head. One tri-axial block was located in the sagittal plane of the forehead; the other two were located above the left and right ear canal, all in planes parallel to the Frankfort plane. Peripheral head acceleration measurements were normalized to the approximate static center of gravity of the driver’s head via an algorithm, which utilized the location of each tri-axial block relative to measured anatomical landmarks as specified by an algorithm by Alem and Holstein, (1977). Torso acceleration was measured using a tri-axial accelerometer (ICS 3031050 ± 50 g) affixed to the occupant with medical adhesive tape and a tightly-fitted elastic wrap bandage at the approximate level of C7-T1 on the anterior torso. Lumbar acceleration in the vehicle X-axis was measured using a single-axis accelerometer (ICS 3031-050 ± 50 g) affixed with medical adhesive to the base of the subject’s lumbar spine at the approximate location of L5-S1.

Volunteers were examined by a chiropractor within hours before and three days after the subject test. The pre- and post-test measures including cervical range of motion, cervical compression tests and point tenderness. Subjects were questioned immediately post-test regarding their perceived experience of the impact. The younger female was exposed

Age Weight [kg] Stature [cm] Seated Height [cm] Anterior Reach [cm] Knee to Buttock (seated) [cm] Knee to Floor (standing) [cm] Head Circumference [cm] Neck Circumference [cm] Neck length [cm]

Younger Lady 35 64.4 160 86.4 75.6 57.2 44.5 53.3 30.5 11.4

Older Lady 60 79.4 163.8 80.6 71.7 64.8 47 57.2 34.3 11.4

Table 2: Anthropometric Data of the Female Volunteers to three additional low-speed impacts the following day for a total of 8 exposures. These tests were conducted in a different vehicle and are not part of the subject study.

Vehicle The vehicle test set-up, data collection and analysis has been described in detail elsewhere and will only be briefly summarized (Ruberté et al., 2014). The target vehicle was a 40 Collision Magazine - Volume 11 Issue 1

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2007 Ford Crown Victoria (VIN: 2FAFP72V47X147799) equipped with leather bucket seats and adjustable head restraints. The rear bumper assembly consisted of a pair of energy-absorbing piston isolators The rear bumper was inspected prior to impact and had no signs of prior structural damage or repairs. The Ford Crown Victoria was fitted with a tri-axial block of ICS 3031-050 (50 g) accelerometers screwed to the front center tunnel ahead of the front seat near the center of gravity of the vehicle. Acceleration data was collected at 10,000 Hz and filtered with an SAE Class 60 filter. Vehicle changes in velocity were calculated from vehicle acceleration data filtered with an SAE Class 180 filter. All axis systems were in accordance with SAE J211 recommended practice with the X, Y, and Z axes directed forward, to the right, and downward, respectively.

Data collection was initiated by a pressure-sensitive tape switch mounted on the vehicle’s rear bumper. A time trap triggered by pressure-sensitive tape switches recorded the vehicle’s velocity. The time trap was placed such that the rear wheels would make contact with the tape switches approximately 0.3 m before barrier contact. Vehicle kinematics are outlined in Table 3.

Test Protocol Subjects were seated in the driver seat of the test vehicle and instructed to adjust the seat and seatback to a comfortable driving position and to maintain a relaxed position prior to impact. Both occupants felt comfortable using the same seatback angle and fore-aft position. They also were instructed to apply the brakes immediately following impact. The older female was exposed to three consecutive impacts with a closing speed up to 1.4 m/s (3.1 mph). The younger female was exposed to five consecutive impacts with a closing speed up to 1.8 m/s (4.0 mph). Each successive impact occurred within 2 to 5 minutes. The volunteers were aware of the impending impact, but could not predict its exact timing. The outside mirrors were covered to eliminate visual cues.

Data Acquisition and Processing Data was collected using a Diversified Technical Systems TDAS Pro. All data was sampled at 10,000 Hz. Head acceleration was filtered with an SAE Class 1000 filter, while thorax and lumbar data were filtered with an SAE Class 180 filter. Data analysis was limited to the sagittal plane, thus, only the longitudinal (x-axis) acceleration was analyzed. Peak amplitude and time to peak amplitude were determined for the crash pulse. The Neck Injury Criteria was calculated according to Boström et al. (1996),

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NIC MAX =maximum first 150 ms (a rel ×0.2+(v rel ) 2 ) and head (Figure 1). The peak thoracic and lumbar accelThe variables arel and vrel represent the relative T1 to head center of gravity x-component of accelerations and velocity, respectively. For the NIC, calculation head and torso acceleration were digitally filtered to 300 Hz (CFC180).

Occupant motion was recorded by a digital video camera placed on the passenger side window.

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esults The dynamic behavior of the Ford Crown Victoria’s rear bumper piston isolators are described in detail in a prior publication (Ruberté, et al., 2014). A summary of the vehicle and occupant responses are presented in Table 3. There was no significant findings between the pre- and post-test clinical evaluations. Results from the examination are included in Table 4. Both volunteers did not complain of pain or symptoms at the conclusion of the tests. The following day the younger female was subjected to three additional impacts on a different passenger vehicle. These included, (1) a rear impact into a barrier (Delta-V = 7.8 mph), (2) a vehicle-to-vehicle rear impact (Delta-V = 7.2 mph), followed by (3) a frontal impact into a barrier (Delta-V = 6.7 mph).

After the second day exposures, the younger lady had minor transient complaints of neck and low back pain which resolved within the hour. All symptoms had resolved at the three-day post-test follow-up examination.

The kinematic response of the female occupants was very similar between tests with similar Delta-V. Head contact with the head restraint occurred in all tests. In general, peak occupant head, thoracic and lumbar accelerations increased as the Delta-V increased. The time to peak acceleration was vehicle first, then lumbar, followed by thorax

eration were below the peak vehicle acceleration in all tests (Table 3). Given the similarities in the occupant responses, only the tests with a change in velocity of approximately 2.0 m/s (4.5 mph) will be discussed. The lumbar traces of the two women nearly overlapped. There was an approximate 20-msec delay between the peak thorax acceleration of the older female compared to the younger female. The head and neck are indirectly accelerated by the motion of the torso. Thus, the delay in the thorax acceleration was also observed in the head acceleration profile. There was no significant difference in the peak head accelerations. The peak thorax acceleration was approximately 1 g higher for the younger female. Occupant acceleration is proportional to the effective mass of the torso loaded by the seatback.

It is likely that torso acceleration occurred earlier in the younger female due to the lower torso mass. There was a likely artifact caused in the lumbar acceleration trace of the older lady during the 2.0 m/s (4.5 mph) impact. This artifact could have potentially been caused by the cables on the instrument package being pulled during the occupant motion.

No significant difference in the occupant response was noted in the restrained versus unrestrained tests during the initial rearward phase of the occupant motion (Figure 2). During the rebound phase of the occupant motion, an abrupt change in the thoracic acceleration was evident at approximately 270 msec for the restrained test. This appears to coincide with the point where the seat belt system began to arrest the forward motion of the occupant by restraining the thorax via the clavicle and the rib cage.

The NIC values in the subject tests were well below the proposed 15 m2/s2 injury threshold.

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Figure 1: Head, thorax and lumbar longitudinal (x-axis) acceleration for both female subjects.

D

iscussion In this study vehicle design factors which may influence occupant motion (i.e. vehicle stiffness, seat stiffness, and geometry) were controlled by using the same vehicle, seatback angle and head restraint position relative to the seat. Similarly, inter-individual differences were minimized by selecting two females with similar anthropometry. Test results showed nearly identical peak head, thorax and lumbar accelerations. The main difference between subjects was a time lag in the torso and

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head acceleration of approximately 25 msec. The observed time lag is likely due to minor differences in anthropometry between the subjects. Although the older lady was slightly taller than the younger lady, the younger lady had a taller seated height and weighed slightly less. The likely lower torso mass of the younger lady is expected to cause less deflection of the seatback padding and springs, as well as, less deflection of the seat frame compared to the older lady. This smaller seatback deflection would decrease the

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dynamic head-to-head restraint distance and contact time and allow the torso to rebound faster.

IIHS evaluated the power leather seat a 2004 Ford Crown Victoria with 4-way height and lumbar support using a BioRID dummy. The 2004 Ford Crown Victoria is in the same model year range as the subject Ford Crown Victoria. However, the test vehicle did not have a lumbar support in the seatback. The 2004 Ford Crown Victoria received a “Good” geometrical rating and a “Marginal” dynamic and overall rating. The IIHS dynamic test simulates a rear crash test with a Delta-V of 4.5 m/s (10.0 mph), which is higher than the DeltaV in the current study. As a result of the impact, the maximum neck forces were high and the head contact time was longer than the threshold of 70 msec. In the subject crash test series, the dynamic response of the head restraint and seat did not appear to be a significant factor due to the low severity of the impact. This is consistent with Farmer et al. (2008) who reported no significant difference in percent of drivers claiming neck injuries for vehicles with minor damage severity regardless of the IIHS seat rating.

NIC has been proposed as a predictor of neck injury potential in low-speed automobile collisions.

Wheeler et al. (1998) reported NIC values between 0.8 and 4.7 m2/s2 for impacts with a Delta-V of 2.2 m/s (5 mph) or less. This is consistent with the NIC values determined in the subject tests. The low NIC values in the current study correlate with the lack of symptoms reported by the occupants and support the 15 m2/s2 injury threshold.

A review of the literature was presented showing inconsistent evidence that female gender and older age are associated with an increased risk of WAD. In very low severe impacts, as in the subject tests, quantitative measures do not support a higher risk of injury to an older female adult compared to a younger adult. Older age had no effect on biomechanical measurements or symptoms reported in any of the tests. Further research is needed regarding the response of older adults and females to lowspeed rear-end collisions.

A

cknowledgments Special thanks to the members of the 2014 ARC-CSI Crash Team Boot Camp for their assistance during testing.

Figure 2: Restrained versus unrestrained head, thorax and lumbar longitudinal (x-axis) acceleration for the younger lady during a rear end impact with a Delta-V of 2.6 m/s (5.8 mph).

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R

eferences 1. Alem, N. M., & Holstein, G. S. (1977). Measurement of 3-D Motion. HSRI, October 1977, 28 p. Report No. UM-HSRI-77-46.

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7. Carlsson, A. et al. (2011). Dynamic kinematic responses of female volunteers in rear impacts and comparison to previous male volunteer tests. Traffic injury prevention, 12(4), 347-357.

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Do “whiplash injuries” occur in low-speed rear impacts? European spine journal, 6(6), 366-375.

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11. Davidsson, J., & Kullgren, A. (2011). Evaluation of seat performance criteria for rear-end impact testing.

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lord National Conference Convention Centre, June 13–16, 2011 (Vol. 22), Paper No. 11-0373.

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(2003). Vehicle and occupant kinematics in low-speed override/underride collisions. Society of Automotive Engineers SAE Technical Paper No. 2003-01-0158.

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Steering Column Loads and Upper Extremity Motions During Low Speed Rear-End Collisions. Society of Automotive Engineers SAE Technical Paper No. 2011-01-0275.

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Roentgenographic findings of the cervical spine in asymptomatic people. Spine, 11(6), 521-524.

18. Hogg-Johnson, S., Van Der Velde, G., Carroll, L. J. et al. (2008). The burden and determinants of neck pain in the general population. European Spine Journal, 17(1), 39-51.

19. Holm, L. W., Carroll, L. J., Cassidy, J. D., et al. (2009).

The burden and determinants of neck pain in whiplash-associated disorders after traffic collisions: results of the Bone and Joint Decade 2000–2010 Task Force on Neck Pain and Its Associated Disorders. Journal of manipulative and physiological therapeutics, 32(2), S61-S69.

20. Ivory, M. A., Furbish, C. J., Hoffman, M. R., et al.

(2010). Brake pedal response and occupant kinematics during low speed rear-end collisions. Society of Automotive Engineers SAE Technical Paper No. 201001-0067.

21. Kahane, C. J. (2013). Injury vulnerability and effectiveness of occupant protection technologies for older occupants and women. Report No. DOT HS 811 766. Washington, DC: National Highway Traffic Safety Administration.

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22. Keifer, O. P., Layson, P. D., & Reckamp, B. C. (2005).

The effects of seated position on occupant kinematics in low-speed rear-end impacts. Society of Automotive Engineers SAE Technical Paper No. 2005- 01-1204.

23. Kleinberger, M., Sun, E., Saunders, J. et al. (1999).

Effects of head restraint position on neck injury in rear impact. In World Congress on Whiplash-Associated Disorders, Vancouver, Canada.

24. Krafft, M. et al. (2002). Influence of Crash Pulse Characteristics on Whiplash Associated Disorders in Rear Impacts--Crash Recording in Real Life Crashes.

Traffic Injury Prevention, 3(2), 141-149.

25. Kraft, M. et al. (2005) Influence of Crash Severity on Various Whiplash Injury Symptoms: A Study Based on Rear-Life Rear-End Crashes with Recorded Crash Pulses. Proc 19th Int. Techn Conf. of ESV, Paper No. 05-0363.

26. Kullgren, A. et al. (2003). Validation of neck injury criteria using reconstructed real-life rear-end crashes with recorded crash pulses. In Proc. 18th Enhanced Safety of Vehicles Conf., 1-13.

27. Kullgren, A. et al. (2007). The effect of whiplash protection systems in real-life crashes and their correlation to consumer crash test programmes. In Proc.

20th Enhanced Safety of Vehicles Conf.(07-0468), Lyon, France, 1-7.

28. Kullgren & Krafft (2010) Gender Analysis on Whiplash Seat Effectiveness: Results from Real-World Crashes. In Proc. IRCOBI Conf., Hanover, Germany, 17-28.

29. Linder, A. et al.(2003) Change of Velocity and Pulse Characteristics in Rear Impacts: Real World ad Vehicle Tests Data. Proc. 18th Enhanced Safety of Vehicles Conf. Nagoya, Japan, 1-9.

30. Liu, C. et al. (2007). Characteristics of crash injuries among young, middle-aged, and older drivers. Report No. DOT HS-810 857. Washington, DC: National Highway Traffic Safety Administration.

31. Lundell, B. et al. (1998). The WHIPS seat-a car seat for improved protection against neck injuries in rear end impacts. In Proc. 16th Enhanced Safety of Vehicles Conf., Paper No. 98-S7, 0-8.

32. McConnell, W. E. et al. (1993). Analysis of human test subject kinematic responses to low velocity rear end impacts. In Vehicle and Occupant Kinematics: Simulation and Modeling (SP-975). SAE Interna-

tional Congress & Exposition, Detroit, MI. Society of Automotive Engineers SAE Paper No. 930889, 21-31.

33. McConnell, W. E. et al. (1995). Human head and neck kinematics after low velocity rear-end impacts. In Proceedings of the 39th Stapp Car Crash Conference (P-299), San Francisco, CA., Society of Automotive Engineers. SAE Paper No. 952724, 215-238.

34. Miller, J. A. A., Schmatz, C., & Schultz, A. B. (1988).

Lumbar disc degeneration: correlation with age, sex, and spine level in 600 autopsy specimens. Spine, 13(2), 173-178.

35. Mills, D., & Carty, G. (2004, June). Comparative analysis of low speed live occupant crash test results to current literature. In Proceedings of the Canadian Multidisciplinary Road Safety Conference, Vol. 14, 1-14.

36. National Highway Traffic Safety Administration.

(2014). Traffic Safety Facts 2012 Data (DOT HS 812 005). Washington, DC. 37. Nielsen, G. P., Gough, J. P., Little, D. M., et al. (1997).

Human subject responses to repeated low speed impacts using utility vehicles. Society of Automotive Engineers SAE Technical Paper No. 970394.

38. Otremski, I. et al. (1989). Soft tissue cervical spinal injuries in motor vehicle accidents. Injury, 20(6), 349351.

39. Roberts, V. & Compton, C., (1993). The Relationship Between Delta V and Injury. Society of Automotive Engineers SAE Technical Paper No. 933111.

40. Ruberté, L., Lantz, S., Thompson, R., et al. (2014).

Low velocity piston isolator testing of a Ford Crown Victoria. Collision, 9(2), 46-53.

41. Siegmund, G. P. et al. (1997). Head/neck kinematic response of human subjects in low-speed rear-end collisions. In Proceedings: Stapp Car Crash Conference (Vol. 41). Society of Automotive Engineers SAE, 357385.

42. Siegmund, G. P. et al. (1999). The influence of head restraint and occupant factors on peak head/neck kinematics in low-speed rear-end collisions. Accident Analysis & Prevention, 31(4), 393-407.

43. Siegmund, G. P. et al. (2005). Variability in vehicle and dummy responses in rear-end collisions. Traffic Injury Prevention, 6(3), 267-277.

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44. Szabo, T. J. et al. (1994). Human occupant kinematic response to low speed rear-end impacts. Society of Automotive Engineers SAE Technical Paper No. 940532.

49. Van Ratingen, M. et al. (2009) The Euro NCAP Whiplash Test. Proc. Of the 21st Enhanced Safety of Vehicles Conf., Stuttgart, Germany, 09-0231.

45. Szabo, T. J., & Welcher, J. B. (1996). Human subject kinematics and electromyographic activity during low speed rear impacts. In Proceedings: Stapp Car Crash Conference. Society of Automotive Engineers SAE. (Vol. 40), 295-315.

50. Viano, D. C., & Olsen, S. (2001). The effectiveness of active head restraint in preventing whiplash. Journal of Trauma and Acute Care Surgery, 51(5), 959-969.

46. Szabo, T. J., Voss, D. P., & Welcher, J. B. (2002). Relationships Between Impact Pulse Duration and Occupant Kinematics in Low Speed Rear Impacts. Society of Automotive Engineers SAE Technical Paper No. 2002-01-0029.

47. Szabo, T. J., Voss, D. P., & Welcher, J. B. (2003). Influence of seat foam and geometrical properties on BioRID P3 kinematic response to rear impacts. Traffic injury prevention, 4(4), 315-323.

48. Temming, J. (1998). Human Factors Data in Relation to Whiplash Injuries in Rear End Collisions of Passsenger Cars. Society of Automotive Engineers SAE Technical Paper No. 981191.

Test Subject

Restraint

Delta-V Vehicle [m/s] apeak [g]

A

None

1.02

None

1.42

None

2.04

Younger None Lady

0.76

B C D E F G H

Older Lady

Older Lady

Older Lady

Younger None Lady

Younger None Lady

Younger None Lady

Younger Lap and Lady shoulder belt

1.35 2.09 2.59 2.59

51. Wheeler, J. B., Smith, T., Siegmund, G. P., & King, D. (1998). Validation of the neck injury criterion (NIC) using kinematic and clinical results from human subjects in rear-end collisions. In Proc. of the International Research Council on the Biomechanics of Injury conference, 26, 335-348.

52. Welcher, J. B., Szabo, T. J., & Voss, D. P. (2001a).

Human occupant motion in rear-end impacts: Effects of incremental increases in velocity change. Society of Automotive Engineers SAE Technical Paper No. 2001-01-0899.

53. Welcher, J.B., Szabo, & Voss, D. P. (2001b). Statistical dependence on human occupant accelerations on vehicle impact measures in rear-end impacts. In Proc. of 2001 ASME International Mechanical Engineering Congress and Exposition, 207.

Crash Pulse Head [msec] apeak [g]

1.8 152 (91 msec)

2.8 117 (56 msec)

4.6 126 (63 msec)

1.4 154 (92 msec)

3.1 123 (67 msec)

5.5 129 (56 msec)

7.6 134 (61 msec)

6.4 130 (76 msec)

Thorax apeak [g]

Lumbar apeak [g]

NICmax [m2/s2]

2.2 1.2 0.8 1.1 (265 msec) (233 msec) (159 msec)

5.1 2.2 2 2.2 (225 msec) (204 msec) (123 msec)

7.5 2.8 3.6 3 (216 msec) (199 msec) (145 msec)

2.6 0.9 1 1.3 (216 msec) (206 msec) (194 msec)

5.2 2.2 2.1 2.3 (211 msec) (221 msec) (136 msec)

7.2 3.5 2.8 1.7 (192 msec) (179 msec) (119 msec)

10.3 4 3.5 3.4 (193 msec) (177 msec) (143 msec)

11.3 4 4 4.1 (187 msec) (181 msec) (148 msec)

Table 3: Vehicle and occupant kinematic response 46 Collision Magazine - Volume 11 Issue 1

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Younger Lady

Older Lady

Flexion

Pre-Test Cervical Evaluation 54.7°

Post-Test Cervical Evaluation 60.3°

Pre-Test Cervical Evaluation 54.3°

Post-Test Cervical Evaluation 55.7°

Extension

79.7°

79.0°

60.0°

67.7°

Left Lateral Flexion

38.7°

38.7°

35.0°

38.3°

Right Lateral Flexion 45.3°

39.3°

40.7°

39.3°

Left Rotation

85.7°

74.3°

65.3°

74.7°

Right Rotation

84.0°

85.0°

72.7°

71.3°

Neutral Cervical Compression Cervical Compression with extension Maximum Cervical Compression Maximum Cervical Compression Shoulder Depression(left) Shoulder Depression (right) Notes

Negative

Negative

Negative

Negative

Negative

Negative

Negative

Negative Negative

Negative

Right SCM tightness Negative Negative

Negative

Negative

Negative

Negative, bone rubbing sensation Negative, bone rubbing sensation Negative

Negative

Negative

Negative

Recent headaches.

Participated in 3 ad- Slept wrong and ditional full vehicle woke up with stiff crash tests the follow- neck. ing day.

Right suboccipital tenderness.

Negative Negative

No symptoms

Took 400 mg of Ibu- Felt immediate neck profen 30 min. prior and low back pain on to impact tests. second day of testing after three additional exposure with average Delta-V of 2.8 m/s (6.3 m/h). Pain resolved within an hour.

Following day woke up with general soreness full spine.

Soreness was gone after two days.

Table 4: Maximum voluntary cervical range of motion and orthopedic evaluation

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. l e e h e w h t at n r u my t

BE CAREFUL WITH WHAT YOU ASK FOR IT MIGHT BITE YOU!

by EriK Carlsson

T

his article is about yet another case in which a carmaker involved in litigation retained a resourceful testing laboratory to disprove the allegation by a law firm that represented an injured vehicle driver.

And, as in the two other crash tests described earlier in Collision, even this crash test resulted in some red faces.

The driver of a small car sustained serious spinal injuries when the roof of her car collapsed at a collision with a deer that jumped across the road. The deer partially entered the interior of the car due to the substantial rearward deformation of the front section of the roof. The accident happened at night on a country road that the driver had entered a moment before the accident. The speed of the car was therefore believed to have been well below the speed limit, 45 mph.

The neck of the driver was subjected to excessive rearward bending which resulted in irrevocable injuries. (The driver, who was pregnant at the time, became a quadriplegic.) A law firm representing the injured driver filed a law suit against the car manufacturer, alleging that the roof of the car she was driving was too weak to offer a reasonable protection in a type of accident that could and should be anticipated, of which a collision with a jumping dear on a country road is very foreseeable, especially 50 Collision Magazine - Volume 11 Issue 1

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at night. As could be expected, the carmaker’s legal team maintained that the car model complied with all mandatory safety standards, and that the plaintiff’s allegation therefore was without merit.

The plaintiff’s attorneys counteracted the defendant’s argument, and reasoned that the carmaker had an obligation to design the car in such a way that it offered its occupants a reasonable protection in accidents that should be anticipated. As an example of a much safer roof design than that of the subject vehicle, the attorneys referred to a car model where the manufacturer had demonstrated through FMVSS 208 dolly rollover tests that their cars had excellent roof strength. [The particular model mentioned by the attorneys is the same as the one depicted in the article “Don’t Look Too Closely!”, Vol. 10 Issue 2 of Collision.] The plaintiff’s attorneys also requested the defendant to supply manufacturing drawings of the roof of the accident vehicle model to be used as exhibit at trial in support of their allegations about the weak roof of the accident vehicle.

The defendant carmaker’s technical staff may perhaps have shared the plaintiff’s negative assessment of the roof because after some legal wrangling and delay, the carmaker did supply roof drawings, but those drawings were of a model equipped with sunroof, which the ac-

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cident vehicle did not have. The sunroof was enclosed in a frame that was attached to both the windshield header and the side rails of the roof. Naturally, the framework behind the windshield header in the model equipped with sunroof added to the roof ’s resistance to a rearward force acting on the roof ’s front edge.

The carmaker’s legal team no doubt expected that once the plaintiff’s attorneys had detected that the submitted drawings showed a roof that certainly had to be stronger than that of the plaintiff’s car, they would allege that the defendant in effect admitted that the roof of her car lacked a reasonable minimum strength. In what probably was an attempt to counteract such allegations, the defense team retained an expert witness from England, who had both engineering and medical training, and who in addition was very familiar with legal disputes about car safety here in the U.S. The expert wrote a detailed report in which he stated that had the roof of the plaintiff’s car been rigid, the front edge of the roof would have cut the deer in half at the impact. This would have caused the lower half of the deer to enter the passenger compartment. The plaintiff’s injuries would then have been worse. Hence the vehicle’s low roof strength was to her benefit, he opined.

The defense legal team was apparently not convinced that the statement by their expert would be sufficient for the defendant carmaker to prevail in court. After all, most potential jurors could no doubt be expected to know that the plaintiff in this case sustained far more severe injuries than what happen to most drivers of cars hitting a deer, except in cases where a startled driver swerves to the side in an attempt to avoid the deer and instead hits a utility pole or a tree (or a house!)

A 17-year-old driver swerved to miss a deer, but hit this Hackettstown house. (photo courtesy of Jay Edwards - Star-Ledger) To demonstrate that the difference in roof design between the two car models would not have had any effect on the outcome of this accident, and that the plaintiff therefore would have been just as injured if she had been driving a car of the model referred to by the plaintiff’s attorneys, the defense attorneys retained a testing laboratory known for its abilities to meet expectations. In this case, to conduct a full-scale “deer” collision test with each of the two car models.

The “car-to-deer” impact tests For the purpose of the requested tests, the testing laboratory created a “deer” by bundling together a number of PVC pipes, said to be partially filled with water and cotton so as to attain the weight of the actual deer, as estimated by the rescue staff that responded to the accident. At the impact tests, the “deer” was suspended in a vertical position, and at a height such that its center of gravity was about even with the front edge of the roof.

The point of impact between the “deer” and the car roof was midway between the car’s centerline and the driver side A-pillar. The test speed was specified to be 50 mph. For reasons not explained, the crash tests were done without dummies in the cars even though the sole issue of the case was the extent of the driver’s injuries, not how the accident happened.

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Test Results The included photos are derived from a videotape of the test obtained by the plaintiff’s attorneys. As can be conclusively determined from the photos, the defendant carmaker’s attorneys were made painfully aware that what they had asked for turned out to be a burningly hot potato.

A Legal Team’s Dilemma?

It is very common in litigation that attorneys for one of the litigants object strongly to some evidence the adversary wants to introduce at the trial. The objection is usually that the evidence the adversary wants to use does not apply entirely to the case, and is therefore misleading, or that the objecting litigant had no prior knowledge about that evidence, and because of this has not had the opportunity to

analyze the evidence. The objecting litigant would therefore not receive a fair trial, it is argued. Whatever the objection may be, the judge has to decide whether or not the disputed evidence should be permitted. Hence this case is very unique as it is obvious that the plaintiff’s attorneys would like to show the video that the defendant had produced, while the defense attorneys most certainly did not want any jury to see the video. It is therefore difficult to imagine what arguments the defense attorneys could possibly make to persuade a judge to exclude their own video.

As could be expected, the case settled, with the conditions of the settlement kept confidential.

Anyone who has some questions about what I have described in this article is welcome to contact me. My e-mail address is erikcarlsson@live.com

Photo 1: The test vehicle at the moment it strikes the “deer.” This is the vehicle model the plaintiff was driving.

Photo 2: The test vehicle a fraction of a second later.

Photo 3: The test vehicle after another fraction of a second.

Photo 4: The vehicle used for comparison at the moment it impacts the “deer.”

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A brief review of the article “A Dirty Trick by A Law of Physics, or What!” (Collision, Spring 2015 issue) My invitation to readers to contact me if they have any questions about cases I have described in Collision has resulted in some interesting correspondence. One case in particular is the one about a full-scale off-set head-on collision crash test between a car and a pickup truck that showed exactly the opposite to what the defendant carmaker that requested the test intended to show with the test. What made the test particularly interesting is not only that the test backfired, but that the plaintiff’s expert discovered before the test that it had been setup in a way that would probably, if undetected, have ensured the desired test result.

Photo 5: The vehicle a fraction of a second later.

Purpose of the test?

The crash test was supposed to show that the driver of a car involved in an off-set head-on collision with a pickup truck could not have swerved to the right before the collision, as the accident reconstructionists who examined the car after the accident said she had. On the contrary, the defendant carmaker’s expert asserted, had she swerved to the right before the impact, the pickup truck would have continued front-end first and deviated to the left following the impact, not spin around and deviate to the right, as it did at the accident.

The Test Setup

The test was set up in such a way that the two vehicles would approach each other at a closing speed of 80 mph, and with the car moving to the right at a 10-degree angle towards an imaginary side of the road, intended to simulate an attempt Photo 6: The vehicle after another brief moment. by the car driver to avoid a head-on collision. Prior to the The “deer” is laying on the ground behind the car. test, the test vehicles were positioned left-front-corner to left-front-corner at the point along the test track were they would collide at the crash test. Although not supposed to do so, the plaintiff’s expert measured the overlap of the vehicles’ front ends and found that it was only 18 inches, six inches less than the actual 24 inches of overlap that had been determined at the examination of the accident vehicles after the accident. When the plaintiff’s expert detected the incorrect vehicle overlap, the testing staff repositioned the test vehicles so that the overlap became 24 inches.

Test Result

Photo 7: The “deer” used for the tests.

Both vehicles span around and deviated to the right, as the accident vehicles did at the accident that was the issue of the case. Hence the test contradicted the defense expert’s claim that the driver of the car could not have swerved to the right before the impact. Indeed, the test confirmed the plaintiff’s expert’s conclusion that the driver had indeed swerved to the right before the impact, as he had estab-

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lished from the right turn position of the right front wheel and the badly bent steering rack in the plaintiff’s car after the crash. [The right front wheel of the test vehicle was not forced to turn to the right by the impact, as the defense expert claimed happened to the accident vehicle.]

Later Discovery When the plaintiff’s attorneys after the crash test received the requested videotape of the test, they discovered that the vehicles’ overlap at the moment of impact had been reduced to 20 inches. The four-inch reduction of the overlap that took place between the pre-test vehicle inspection and the crash suggests that the overlap at the crash would have been only 14 inches if the vehicles had not been repositioned before the test so as to increase the overlap by six inches. The collision that began as an offset frontal collision would in such a case no doubts have proceeded into a sideswipe due to the small overlap, as the test was supposed to show.

What explains the reduction of the overlap? A suggested explanation for the reduction of the vehicles’ overlap between that measured before the test and the one at the impact, as later discovered, deals with the likely setup of the test. The car was at the crash test accelerated from zero to 40 mph along the test track depicted in the article. The force required for the acceleration was generated by a stationary power-winch and transmitted to the car via a long steel cable running along the test track. The cable, connected to the underside of the car, extended to the rear of the car to a point beyond the place to which the car was pulled back before the acceleration began. From there, the cable passed through pulleys that redirected the cable to the front, to where the pickup truck had been pulled back before the start of the acceleration. A pulley close to intended impact point directed the cable connected to the pickup truck so that the pickup truck would be pulled in a straight line towards the intended point of impact.

With the described setup, the test vehicles would in theory, and only in theory, move in unison and travel the same distance during the acceleration run. In reality, the pickup truck would move a somewhat shorter distance than the car. Prior to the start of the acceleration of the test vehicles, the cable used for the pickup truck had to be slack and laying on the ground. The forward movement of the pickup truck did therefore not begin at the same moment the car began to move forward. Not until the slack in the cable between the car and the pickup truck had been taken out would the pickup truck start moving. How far the car had moved at that point depends on how much slack there was in the cable when the vehicles were at rest before the start of their acceleration.

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There is yet another factor, and which may have played an even bigger role. Steel cables are elastic and will stretch when subject to a pulling force. The increased length due to the stretching is proportional to the transmitted force and the length of the cable. (The force in the cable that accelerated the pickup truck equaled the weight of the truck times its acceleration in g.) The increased length of the cable due to its stretching added to the effect of the initial slack, and reduced further the distance the pickup truck moved during the car’s acceleration.

As can be deduced from the photos shown on page 64 in the Spring 2015 issue of Collision, had the pickup truck been about a foot or so farther back at the moment of impact, the vehicle overlap might have been small enough to cause the collision to progress into a sideswipe, as was the purpose of the test. A higher rate of acceleration of the pickup truck would have increased the stretching of the cable and thus caused the impact to take place farther to the right than shown in the photo. However, since both vehicles were accelerated simultaneously, it is likely that the power winch was running at maximum power during the vehicles’ acceleration, and that the stretching of the cable could therefore not have been increased any further.

It may be argued that the stretching in the steel cable during the acceleration of the pickup truck would have no effect on the vehicles’ position at the time of the impact because the acceleration was reduced to zero when the vehicles reached the desired speed. The force in the cable was therefore reduced to zero, enabling the cable to regain its normal length before the impact. This would be correct if the rate of acceleration was gradually reduced and slowly approached zero as the speed of the test vehicles approached the desired speed. The stretching of the cable would in such case also have been down to zero at the moment the vehicles reached the intended speed. However, if the power-winch was running at full power until it reached the pre-determined wire speed, with the power at that point abruptly cut so as not to overshoot the specified speed, the cable would have slackened immediately and the “recovery” of the cable’s natural length would have been from the winch end. The stretching of the cable during the acceleration would therefore not have been able to pull the pickup truck an additional distance forward while it approached the intended point of collision.

Anyone who has some questions about what I have described in this article is welcome to contact me. My e-mail address is erikcarlsson@live.com

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. ew i v e R l a i or t i d E

Streamlining Accident Investigation Larry Trojak Trojak Communications

T

he push to increase the speed and accuracy of accident investigations — and improve the safety of the personnel gathering the data at the scene — is an ongoing one, driven by research showing far-reaching benefits of doing so. Consider the following: closing three lanes of an interstate like I-35 in any of the major metropolitan areas it traverses (from Minneapolis to San Antonio) can adversely affect that area to the tune of $45,000 an hour. Literally adding insult to injury: if a major highway incident is ongoing for an hour, there is an 85% chance of a secondary collision as a result of traffic backup. With that in mind, the cleanup needs to be fast, the evidence/data gathering effort has to be accurate and workers at the scene must be kept safe — a tall order to be sure. But, as companies like Steve McKinzie’s CSI Mapping have found, technology, including the use of GNSS-based equipment, is changing the way things are being done at such sites and, in a whole lot of cases, bringing that tall order down to size.

From Patrol to Public Wearing the hats of both private practitioner and a former Kansas Highway Patrolman, Steve McKinzie brings a unique perspective — and a wealth of experience — to accident scene investigation. In the quarter century he’s been reconstructing crashes, McKinzie has witnessed significant change — moves largely driven by advances in technology.

“When I started this business, the overall challenge facing any accident reconstruction professional was finding ways to improve how physical evidence was being documented,” said McKinzie. “That meant improving not only our accuracy during the data documentation process, but also how quickly we could do it. Today, we use total stations, laser scanners and GNSS-based instrumentation such as rovers, bases and receivers to do the work that used to be done with a measuring wheel or tape measure and hand-written notes in a pad. As a result, the how of accident investigation work has been streamlined, and now, using the data captured at the

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scene, in most cases, we are better at answering why it happened.”

Time is of the Essence There is a real motivation for wanting to improve efficiency in the data documentation facet of any investigation. The longer it takes to collect and document data, the greater the risk of evidence being inadvertently destroyed.

“When traffic is backed up for miles and the push is on to get things moving again, something has to give — and most often it’s the evidence,” said McKinzie.

“Some physical evidence may only last hours; it can be destroyed when the fire trucks arrive. So it is imperative that things get done quickly, accurately and safely.”

With that focus in mind, McKinzie was recently consulted by a Federal Highway Administration (FHWA) contractor to examine the emerging technologies that can be beneficial in improving what’s known as quick scene clearance (QSC). As part of that study, he looked at the broadest possible range of techniques and technologies —everything from tape measure and roller tape, to different total station approaches (infrared measuring to a prism, reflectorless, robotic, hybrid), to GPS, to photogrammetry and laser scanning.

“Another overriding goal of this effort was to improve the safety of first responders at a crash site,” said McKinzie.

“It’s no secret that there are people getting killed trying to help others. So the more quickly we can get them to the site, get the site processed — rendering aid, removing injured parties, collecting and measuring physical evidence, extrication of the vehicle, final cleanup, and so on — the faster we can get the flow of traffic back to normal and reduce the chance of secondary collisions.”

Test Results Are In One of the things that was looked at closely in the study McKinzie headed up involved asking: “In employing this technology, does the user have to get in the roadway

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and exposed to traffic?” Not surprisingly, a measuring wheel and tape measure were expected to score very low because of the user’s high exposure to danger.

“Similarly, using an infrared total station did not fare not much better because the rod person has to be out where the physical evidence is,” said McKinzie. “Even though there might be road or lane closures, that doesn’t necessarily constitute a safe environment. As we see on the news all too often, people drive through cones, around fire trucks, into police cars, and so on.”

According to McKinzie, he found some of the overall results a bit surprising. The Topcon GLS-2000 laser scanner they tested, while very effective, scored lower because of the time needed to scan (due to multiple movements of the instrument), low reflectivity from the vehicles’ chrome and glass, and an overabundance of data. Terrestrial based photogrammetry also didn’t fare so well because of its distance limitations and the traffic exposure factor.

“A Sokkia SX robotic total station scored high even though the operator is still exposed to a level of danger while he or she is collecting evidence,” said McKinzie. “But, because the instrument is so fast, the overall exposure time is lower — so it did well. A Sokkia CX-105 reflectorless total station also fared very well but, because it can’t see the other side of a tractor trailer, it has to either be moved or personnel have to wait until the debris and the vehicles have all been extricated from the scene and then measure the physical evidence.”

To address that last issue, McKinsey has advocated using an inverted fluorescent spray to paint spots on the pavement in the location at which they would have put the prism pole. “Then, after the vehicles are removed from the scene,” he said, ‘the roadway can be opened and the crash investigation team can come back and shoot the physical evidence reflectorless from the side of the road without ever having to step out into traffic The technology that showed the most promise is an unmanned aerial system (UAS) or drone. McKinzie said the fact that it can be launched at the crash site and fly the typical 100-foot wide by 2,000-foot long

path —while producing centimeter grade accuracy — in 5-10 minutes is exciting. “And the personnel exposure issue is totally removed from the equation,” he said. tender Best of Both Worlds Because of its Kansas City-area location, CSI Mapping works regularly with the Missouri Highway Patrol to continue developing and refining its GNSS program. With that in mind, one of McKinzie’s focuses of late has been to find a solution that is more adaptable to the already tech-heavy cruisers its patrolmen drive.

“The Missouri Highway Patrol routinely uses GNSS to measure crash sites, as long as they can get a cell signal out to a MoDOT reference station,” said McKinzie. “But there’s no denying that carting around such bulky equipment hampers their efforts. So we worked with Sokkia to find them a good compact workable solution and got it in the Sokkia GCX2 receiver, a screw-together two-meter pole and a Sokkia S-10 mobile field controller. The impressively tiny package replaces two huge orange cases, a wooden tripod, a long pole and a couple of prism bags. An officer can put this in his car and as long as he has a signal available, rarely a problem given MoDOT’s extensive reference network, he has dime-sized accuracy.”

In work it conducts for the Highway Patrol directly, CSI Mapping has helped enhance both the speed and accuracy of its efforts through the use of hybrid positioning technology — essentially marrying their optical system with GNSS. “If we are investigating the crash of a commercial vehicle and a car, for example, our Sokkia DX total station is positioned in a spot that is safe, yet able to document the largest amount of physical evidence.

Ordinarily, for areas which it cannot reach, it would have to be moved or re-sectioned back into the map.

With the hybrid system, however, we can simply switch over to GPS and, with RTK accuracy, keep mapping without even having to see the total station.

That easily reduces the 15 to 20 minutes it takes to move and/or re-section. And because the total station

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is robotic, it is always operating in tracking mode so we can map as fast as we can walk.”

Away From the Scene Currently electronic documentation of data from the Sokkia GCX2 is handled using Topcon MAGNET Field software.

A map is then processed, first as simple 2D line work, then, if necessary, enhanced to 3D in either iMap 360 (formerly called MapScenes Forensic Cad), CrashZone, Ares360 or Visual Statement.

“Normally, the majority of maps that are produced stay in a 2D world,” said McKinzie. “All that is needed is a flat map that goes over a piece of paper and becomes part of a report published to the public. However, if there is a case in which, say, someone is trying to assess sight visibility distance, that can’t be done with a 2D map, you need that 3D model and that’s where we come in. We go back to the scene of the accident, create the 3D model of the roadway using either a Hybird station, GNSS or scanner, answer any questions about physical evidence and vehicle speed in order to do a Dynamic Sight Distance Analysis (DSDA) to determine where things were as the two (or more) parties involved approached each other.”

Once the surface model is created, the reconstruction tells McKinzie and his team where the collision occurred, how far each vehicle moved post-impact, what the dynamics of that phase of the collision were, and more.

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“We are then able to calculate the speed of the vehicles at impact,” he said. “From that data we are able to create the DSDA. We ‘drive’ the vehicles over the roadway model we created at those known speeds and, from that, are able to see what each driver had available to him to see.”

Making Converts McKinzie and CSI Mapping’s time is divided, with the bulk of their efforts doing work like that described above and the rest spent helping educate accident scene investigators around the country about the technology that is available to help them do their jobs in the safest most expeditious manner possible. Because the technology does not come cheap, it is rarely an easy sell.

McKinzie said a typical scenario has a Patrol Commander asking: “What will I gain by spending tens of thousands of my budget dollars on equipment designed to minimize a loss, when that loss does not directly affect me or my department’s budget?”

“The answer I give that chief,” he said, “is that, over the ten years he was commander and his teams were equipped for this kind of work, he saved millions of dollars in closures that were either avoided outright or minimized, and he made the highways safer — possibly even saving lives in the process — by clearing things up quickly and eliminating secondary collisions. That’s a pretty nice legacy.”

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.

E

R R E E

P

D

WE E I V

Using Ford EDR Pre-crash Stability Control System Data Sgt. John Bruno Richard R. Ruth, P.E. Michigan State Police

T

Ruth Consulting LLC

his papers describes methods for the reconstructionist to use stability control system EDR data to: 1. Calculate the speed at impact more precisely than with pre-crash speed data alone

2. Calculate the distance to impact after first brake application or steering input in reaction to a developing situation 3. Calculate the lateral distance a vehicle moved during an evasive steering event based on a. Stability control lateral acceleration b. Yaw rate 4. Calculate the change in approach angle at impact relative to straight down the road The example used to explain the calculations is from a 2012 Lincoln MKZ (Figure 1), but stability control system data began to phase in for 2009’s and is in nearly all 2012 and later Ford event data recorders (EDR’s). The methodology can also apply to data found in some 2012 and later Toyota vehicles, and some 2006 and later Chrysler vehicles with yaw rate data.

The example used is from an actual crash where a 2012 Lincoln MKZ rear-ended a large commercial vehicle in a snow storm. The speed control was set at 58 mph, but the driver took evasive action by braking and swerving to the left in the last one second prior to the crash. The issue under investigation was whether the MKZ driver reacted or not, and if so when the truck’s presence was perceived.

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Before any calculations are made we must verify the EDR recording is from our crash. The first page of the CDR report indicates a locked frontal event. It is a complete recording. The key cycles are 3760 at imaging versus 3759 at the time of the crash. The delta-V is -35.23 mph longitudinal and -3.02 lateral mph over 173ms, which is consistent with the last reported speed of 55.4 mph and the truck still moving, and the significant crush damage observed. The driver and

passenger airbags and pretensioners were deployed which is consistent with the physical evidence. The “Speed Control Telltale” was reported as “On” at -1 second and the reported 0% accelerator pedal position while maintaining a constant speed is consistent with the speed control being on. There is no doubt that this recording comes from this crash. The precrash speed, accelerator pedal, and brake data at 0.5 second intervals is shown in Figure 2.

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Figure 1: 2012 Lincoln MKZ (above)

Figure 3: Ford stability control system data (right)

Figure 2: The pre-crash speed, accelerator pedal, and brake data at 0.5 second intervals The Ford stability control system data at 0.1 second intervals including steering wheel angle, longitudinal acceleration, lateral acceleration and yaw rate is shown below. The data from -5.0 to -2.0 is relatively constant and is therefore not shown due to space constraints. Note that this was a known left swerve and that the polarity of Ford steering, yaw rate, and stability control lateral acceleration is opposite of the SAE right hand rule. Positive steering is left, and positive yaw rate is counter-clockwise, as confirmed in SAE 2014-01-0504 “Accuracy and Timing of 2013 Ford Flex Event Data Recorders”. The 2013 Ford Flex and 2012 Lincoln MKZ both use an Autoliv RC6 ACM, and while the authors acknowledge that it has not been confirmed on every Ford model, it is our belief that it likely applies to all Ford & Lincoln models. The SAE paper indicated that the 66 Collision Magazine - Volume 11 Issue 1

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lateral acceleration CAN bus signal and reference instrumentation appeared to agree, implying that the polarity was normal SAE convention. The paper did not explicitly confirm that the polarity as recorded in the EDR was standard SAE polarity. The authors have examined this and other real world files and note that the sign of the lateral acceleration during swerves is the same sign as the steering and yaw, which indicates that all three are opposite of SAE polarity and are positive left/counterclockwise and negative right/clockwise.

The Bosch Crash Data Retrieval system provides for saving the data in a Comma Separated Value or “CSV” format to allow it to be used in a Microsoft Excel spreadsheet.

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alculating the speed at impact based on EDR “Stability Control Longitudinal Acceleration” The methodology begins by reviewing the precrash data at 0.5 second intervals and identifying the last data point when the speed was still relatively stable. The change in speed will be calculated from the longitudinal acceleration, but to get an absolute speed at impact, the change in speed must be subtracted from the last stable speed data point taken at 0.5 second intervals. Pre-crash data indicates that at -1.0 second data point (recorded sometime between -1.5 and -1.0 seconds to impact) the speed control was on, the service brake was off and the vehicle was traveling at a steady 58.4 mph. The 58.4 mph will be used as the starting point for using the stability control system longitudinal acceleration to calculate the speed at 0.1 second intervals.

The “Stability Control Longitudinal Acceleration” shows a change from very small magnitude negative values of -0.018g (indicating no braking yet) at -1.0 seconds to the onset of braking (-0.187g) at -0.9 seconds. From -0.9 seconds to 0.0 seconds the longitudinal g values are indicative of constant braking with g values ranging from -0.160g to -0.206g. These values are typical for maximum braking on a freshly snow covered road which we have in this case. The methodology should work equally well on dry pavement, the authors are simply explaining why the longitudinal g values for this specific example are so low. The “service brake” also indicates an on status from -0.5 seconds to 0.0 seconds. Calculations for longitudinal deceleration will begin at -1.0 seconds and continue to 0.0 seconds.

The 2012 MKZ speed is only reported to the next lower km/h and then rounded to MPH to the nearest 0.1 mph.

Time -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0

Vx’ Previous time sample velocity (ft/sec) 85.614 + 85.012 + 84.349 + 83.834 + 83.238 + 82.646 + 81.992 81.367 80.842 80.327

+ + + +

Since there is a series of 11 calculations, we will choose to carry 3 decimal places during the calculations so as not to accumulate rounding error before finally rounding to one decimal place again at the end. Use the following formula to calculate the velocity of the vehicle at each sample interval as it decelerates.

Formula used for this calculation is: Definitions: Vx = longitudinal velocity in ft/sec at the end of the interval (for the current time sample) Vx’= longitudinal velocity in ft/sec at the start of the interval (from the previous time sample) g = current time sample acceleration in g’s t = time interval between samples in seconds Start speed calculation after -1.0 sec at 58.4 mph = 85.614 ft/sec = Vx for -1.0 and Vx’ for -0.9 seconds.

The -0.9 line is reporting changes between -1.0 and -0.9.

This calculation assumes the – 0.187 g value was constant over the interval -1.0 to -0.9 sec. The authors believed the simplicity and ease of understanding of the rectangular approximation (constant magnitude over the interval) are more important than the slight added accuracy that would be obtained from a trapezoidal approximation (Table 1).

0.0 is the last reported data point, but the crash could be up to 0.099 seconds after the point labeled 0.0. Any asynchronous pre-crash data point can be up to one interval between data points “old” when the crash occurs. (Refer-

Current time Duration of time Convert g’s to Vx Velocity of current sample g value sample (sec) t ft/sec2 time sample (ft/sec) -0.187 x 0.1 x 32.2 = 85.012 -0.206 x 0.1 x 32.2 = 84.349 -0.160 x 0.1 x 32.2 = 83.834 -0.185 x 0.1 x 32.2 = 83.238 -0.184 x 0.1 x 32.2 = 82.646 -0.203 x 0.1 x 32.2 = 81.992 -0.194 -0.163 -0.160 -0.186

x x x x

0.1 0.1 0.1 0.1

x x x x

32.2 32.2 32.2 32.2

= = = =

81.367 80.842 80.327 79.728

Table 1: Calculation of velocity at a specific time sample www.collisionpublishing.com

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ence: IPTM “Event Data Recorder Use in Traffic Crash Reconstruction” curriculum 2016). The calculation can be continued using the average deceleration over the 3 data points to see what the lowest possible value at impact could be: +0.1

79.728 +

-0.170 x

0.1 x

32.2 =

79.18

This calculation indicates the speed at impact was between 79.1 ft/sec and 79.7 ft/sec (54.0 to 54.4 mph). Starting from 58.4 mph, the vehicle lost between 4.1 and 4.5 mph between the start of braking and impact based on the stability control system data. Considering the +/-4% accepted pre-crash speed data error rate (reference Chidester 1999 “Recording Automotive Crash Data”) in the 58.4 mph, this increases the range by +/- 2.3 mph to 51.7 and 56.7 mph.

Prior to this method, the authors had calculated speed at impact using the IPTM “Speed at Impact Worksheet” that starts from the last pre-crash data point, then considers that the crash could have occurred any time from immediately after the data point was written up to one interval between data points later. Further accounting 5% for wheel slip during hard braking taking place when the last data point is recorded, and the accepted +/-4% accuracy of speed data, would yield a range of possible speeds at impact of 53.7 to 60.3 mph. The old method contains an inherent uncertainty in the braking time of one interval between pre-crash speed data points of 0.5 seconds, while the new method contains the lesser uncertainty in the braking time of one interval between stability control system data points of 0.1 seconds.

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alculating the distance covered during braking based on EDR “Stability Control Longitudinal Acceleration” This calculation will use the Vx from the previous section and multiply velocity times time to get distance.

This calculation is integrating the velocity versus time curve to get the area under the curve; it uses a rectangular approximation (versus the slightly more accurate but more complicated trapezoidal approximation that considers acceleration between data points) for each reporting interval.

The additional formula is:

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Vx = longitudinal velocity in ft/sec for the current time sample t = time interval between samples in seconds Start with the indicated speed of the last sample of no braking. In this example case it is 58.4 mph (85.614 ft/ sec) at -1.0 second (Table 2).

82.3 feet is the distance moved between -1.0 and 0.0, the last data point before impact. Of course, it is possible that the crash occurred up to 0.099 seconds after the 0.0 data point was taken, so the total distance could be higher: +0.1

82.334 +

79.182 x

0.1 =

90.252

The distance traveled during braking is between 82.3 and 90.3 feet.

A way to check this calculation is to use the average starting speed of 58.4 mph (85.614 ft/sec) with the last reported speed of 55.3 ( 81.070 ft/sec) mph =83.3 ft/sec times 1 second to get 83.3 feet.

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alculating the lateral displacement of a vehicle based on EDR “Stability Control Lateral Acceleration” This calculation assumes the vehicle was initially going straight down a straight road, which you can verify by observing values near zero in the steering wheel angle data, if available, followed by a strong input to one side.

Curved roadways would require a more complex calculation. While the steering wheel angle is useful to see when the driver first began their input, it would require complex vehicle modeling to calculate vehicle movement from steering data alone. The stability control system lateral acceleration data will be used to calculate the lateral movement.

Look at the “steering wheel angle” data to find where the driver input indicates the beginning of the swerve. Then look at the lateral acceleration g’s and see where either the sign changes or the magnitude begins to increase, usually 0.2 to 0.3 seconds after the steering input began based on the authors’ experience in reviewing Ford EDR’s with this data in known swerves. The time reading before the lateral acceleration begins to ramp up will be used to start the calculations.

In this example the steering wheel angle changes from minor negative readings prior to -1.2 seconds to +1.1° at -1.1 seconds. The steering wheel angle increases to -0.6 seconds, and while the angle reduces near the end, it still stays positive. For the 2009 Ford RC6, later RC6X, RC7

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Time

Dx’ Previous time sample displacement distance (ft)

-1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0

0.000 8.501 16.936 25.319 33.643 41.908 50.107 58.244 66.328 74.361

+ + + + + + + + + +

Velocity of current time sample Vx (ft/sec) 85.614 85.012 x 84.349 x 83.834 x 83.238 x 82.646 x 81.992 x 81.367 x 80.842 x 80.327 x 79.728 x

Duration of time sample (sec) t 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Cumulative distance moved (ft) Dx 0.000 8.501 16.936 25.319 33.643 41.908 50.107 58.244 66.328 74.361 82.334

= = = = = = = = = =

Table 2: Calculation of cumulative distance moved Dx and AB10 modules, positive steering, lateral acceleration, and yaw rate all mean a left swerve.

The “Stability Control Lateral Accelerations” from the -1.1 first change in steering to -0.8 seconds are all less than 0.06g and change sign. At -0.7 second it was still only 0.045g and then we begin to see a significant increase to 0.119g at -0.6 seconds indicating the vehicle is now responding to the steering input. The authors recommend beginning the calculations at -0.7 seconds. Including the very small values between when the steering input began at -1.1 and when the vehicle response begins after -0.7 would not be expected to significantly affect the calculation. From the starting point, we will first calculate velocity and then later use the velocity to calculate the displacement. Use the following formulas to calculate the lateral velocity and sideways displacement gained.

Time: -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0

Previous sample lateral Current time velocity Vy’ (ft/sec) sample g value 0.000 + 0.045 x 0.145 + 0.119 x 0.528 + 0.105 x 0.866 + 0.193 x 1.487 + 0.225 x 2.212 + 0.137 x 2.653 + 0.112 x 3.014 + 0.212 x

Definitions Vy = lateral velocity of current time sample in ft/sec Vy’ = previous time sample lateral velocity in ft/sec g = lateral acceleration current time sample in g’s t = time duration of sample in seconds As before, the crash may occur up to 0.099 seconds after the last data point. The g value assumed is the average of the last 3 data points: +0.1

3.697 +

Duration of time sample t (sec) 0.1 x 0.1 x 0.1 x 0.1 x 0.1 x 0.1 x 0.1 x 0.1 x

0.154 x

0.1 x

32.2 =

4.192

Convert G’s to ft/sec2 Cumulative lateral velocity Vy (ft/sec) 32.2 = 0.145 32.2 = 0.528 32.2 = 0.866 32.2 = 1.487 32.2 = 2.212 32.2 = 2.653 32.2 = 3.014 32.2 = 3.697

Table 3: Calculation of the cumulative lateral velocity Vy www.collisionpublishing.com

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Then use the velocity calculated above to calculate the cumulative distance moved. Just as with the longitudinal data, this is integrating the area under the velocity versus time curve using a rectangular approximation, versus the slightly more accurate but more complicated trapezoidal approximation that considers acceleration between data points (Table 4).

Definitions Dy = lateral displacement distance of current time sample in feet Dy’ = previous time sample lateral displacement distance in feet Vy = lateral velocity of current time sample in ft/sec t = time duration of sample in seconds As before, the crash may occur up to 0.099 seconds after the last data point: +0.1

1.461 +

4.192 x

0.1 =

1.880

Assuming this vehicle had 0 mph lateral velocity to begin with, it gained 3.6 ft/sec in 0.7 seconds of time and the distance covered was 1.5 feet. If the crash occurred 0.099 seconds after the data point at time 0, then the sideways velocity would be 3.9 ft/sec and the displacement would have been 1.9 feet.

C

alculating the lateral distance moved based on the EDR’s “Yaw Rate” The best way to calculate the lateral distance movement is by using the stability control lateral acceleration data, because the sensor is measuring changes in motion of the EDR which is near the center of gravity.

Time: Previous time sample lateral displacement distance (ft) Dy’ -0.7 0.000 + -0.6 0.015 + -0.5 0.068 + -0.4 0.155 + -0.3 0.304 + -0.2 0.525 + -0.1 0.790 + 0.0 1.091 +

Chrysler records yaw rate; lateral acceleration data is not available. Yaw rate can also be used to calculate a change in heading, and IF we assume that there is no slip angle, then heading is also the direction the vehicle is traveling, and we can calculate a lateral movement from yaw rate.

Since Ford has both, we can use this example to understand how accurate a calculation of lateral movement from yaw rate is during a sudden swerve.

Yaw rate in degrees per second times time gives the change in heading of the vehicle. In a slow lane change, the heading and the direction of movement of the center of mass are very close to one another, but in a sudden swerve the path of the center of mass may lag behind the direction the nose of the car is pointing due to compliance in suspension bushings, body mounts, and tires. In an extreme yaw, a car could be pointed 90 degrees sideways but still moving near its original direction of travel.

Formulas used to calculate the lateral distance moved based on yaw rate begin with calculating the incremental yaw angle for each interval, then adding them to get the cumulative yaw angle, and then using the sine of the cumulative yaw angle times the distance traveled for each interval to get the sideways motion.

The yaw rate data in this case begins to increase from 0.75 deg/sec to 2.62 deg/sec between -1.0 and -0.9 sec. We will start the yaw rate calculation at -1.0 sec. The yaw response to the steering input that starts at -1.1 appears sooner than the lateral acceleration response; therefore, the calculations start at different times.

First calculate the distance moved by taking the changes in velocity calculated earlier from the longitudinal acceleration section to get a velocity for each interval, then multiply it by the time interval to get the distance traveled in that interval (Table 5).

Current time sample lateral Duration of time Cumulative lateral disvelocity (ft/sec) Vy sample (sec) t placement distance (ft) Dy 0.145 x 0.1 = 0.015 0.528 x 0.1 = 0.068 0.866 x 0.1 = 0.155 1.487 x 0.1 = 0.304 2.212 x 0.1 = 0.525 2.653 x 0.1 = 0.790 3.014 x 0.1 = 1.091 3.697 x 0.1 = 1.461

Table 4: Calculation of cumulative lateral displacement Dy 70 Collision Magazine - Volume 11 Issue 1

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Time Definitions L = average longitudinal distance move per time interval in feet Vx = longitudinal velocity during that interval t = time sample duration in seconds Time -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 +0.1

Velocity Vx Time interval Longitudinal (ft/sec) t (sec) distance moved L (ft) 85.614 x 0.1 = 8.561 85.012 x 0.1 = 8.501 84.349 x 0.1 = 8.435 83.834 x 0.1 = 8.383 83.238 x 0.1 = 8.324 82.646 x 0.1 = 8.265 81.992 x 0.1 = 8.199 81.367 x 0.1 = 8.137 80.842 x 0.1 = 8.084 80.327 x 0.1 = 8.033 79.728 x 0.1 = 7.973 79.182 x 0.1 = 7.918

Table 5: Calculation of the longitudinal distance moved L Now use the following formula to calculate cumulative yaw rate:

-0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 +0.1

Previous sample cumulative yaw angle C’ 0.000 + 0.262 + 0.449 + 0.624 + 0.674 + 0.699 + 0.986 + 1.523 + 2.160 + 2.772 + 3.247 +

Current sample Yaw rate (degrees/ sec) Y 2.62 x 1.87 x 1.75 x 0.50 x 0.25 x 2.87 x 5.37 x 6.37 x 6.12 x 4.75 x 5.75 x

Time sample duration (sec) t 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

= = = = = = = = = = =

Current sample cumulative yaw angle C (degrees) 0.262 0.449 0.624 0.674 0.699 0.986 1.523 2.160 2.772 3.247 3.822

Table 6: Calculation of the cumulative yaw angle C Next use cumulative yaw calculated above in the following formula to calculate the lateral distance moved during the swerve (Table 7): Definitions Dy = current time sample cumulative lateral distance moved in feet Dy’ = previous time sample cumulative lateral distance moved in feet C = current time sample cumulative yaw angle in degrees

Definitions C = current time sample cumulative yaw angle in degrees

L = average longitudinal distance moved per time interval in feet

C’ = previous time sample cumulative yaw angle in degrees Y = yaw rate in deg/sec Y * t = the incremental yaw angle for this interval t = time sample duration in seconds

Figure 4: Diagram of cumulative yaw forumula www.collisionpublishing.com

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Time -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0.0 +0.1

Previous sample cumu- Sin of current sample lative lateral distance cumulative yaw angle C moved (ft) Dy’ (degrees) 0.000 + 0.0046 x 0.039 + 0.0078 x 0.105 + 0.0109 x 0.196 + 0.0118 x 0.294 + 0.0122 x 0.395 + 0.0172 x 0.536 + 0.0266 x 0.752 + 0.0377 x 1.057 + 0.0484 x 1.466 + 0.0566 x 1.897 + 0.0666 x

Distance moved during Cumulative lateral distime sample L (ft) tance moved Dy (ft) 8.501 8.435 8.383 8.324 8.265 8.199 8.137 8.084 8.033 7.973 7.918

= = = = = = = = = = =

0.039 0.105 0.196 0.294 0.395 0.536 0.752 1.057 1.445 1.897 2.425

Table 7: Calculation of cumulative lateral distance moved Dy Continue these calculations to time 0 to determine a total lateral distance moved during the steering input. In this case the total lateral distance moved is 1.9 to 2.4 feet.

This is the maximum value assuming the vehicle’s center of mass direction is perfectly tracking the heading change (i.e., that there is no slip angle). Currently, not enough research has been done to determine how closely the center of mass tracks the vehicle’s heading in sudden swerves.

What is known is that vehicles have compliance from tires, suspension bushings and body mounts that initially absorb steering inputs until the vehicle responds. In this case, both lateral acceleration and yaw rate were available at 0.1 second intervals. The lateral acceleration represents changes at the center of mass and is considered more accurate than sideways motion from yaw rate. In this case, the sideways motion calculated from yaw rate was 1.9 to 2.4 feet versus the 1.4 to 1.9 feet calculated from stability control lateral acceleration. For this specific vehicle, you would have to multiply the movement from yaw rate by a slip angle adjustment factor of 0.77 to get the value obtained from lateral acceleration.

This was a sudden swerve, but is unusual in that the input does not increase through impact; it peaks at -0.6 and then reduces. There could also be significant vehicle operational conditions differences and model to model differences.

Until more research has been completed, the authors are observing that the ratio is clearly less than 1:1 and more than 0.5:1, and suggesting taking approximately three quarters of any lateral motion value calculated taken from yaw rate alone in a sudden swerve would be better than simply using a 1:1 ratio.

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alculating Change in Approach Angle The lateral movement gives insight into the change in approach angle for use in momentum and velocity vector calculations. The “final approach angle” is determined by the amount of side movement in the last 0.1 second to impact relative to the forward distance traveled. From the earlier calculations, the vehicle moved forward 7.97 feet (taken from page 8 at 0.0) in the last 0.1 second interval and moved an incremental 0.37 feet sideways (taken from page 7, 1.461 at 0 less 1.091 at -0.1).

Taking the arc sine of (0.37/7.97) yields a center of gravity path of 2.66 degrees left of the straight roadway.

Formula (path change from Lateral Acceleration) This compares to 3.25 degrees (from Page 9 at 0.0) in heading change from yaw rate. This comparison is useful since Chrysler currently only records yaw rate, and crash reconstructionists need to understand that in sudden swerves there is likely to be a slip angle (or “difference”) between heading and the approach angle. Since Ford gives us both lateral acceleration and yaw rate, we can calculate the slip angle and begin to develop a base of knowledge of slip angle under different circumstances. The empirical data in this case suggests the “correction factor” from heading change from yaw rate to approach angle change is to multiply the heading change by 0.82. Again, there could be significant operational conditions variations or model to model variations and this is only a single example.

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... it was possible to calculate the reduction in speed of the vehicle between when the g’s first began to increase (assumed to be brake application) and impact.

Figure 5: Diagram showing lateral movement

U

sing a Microsoft Excel Template Since the Ford data can be saved as a CSV file, it can then be resaved as an excel .xlsx spreadsheet.

Presuming that all 5 columns were provided, the authors have created a template that will calculate the values by simply pasting in the CSV data into the template.

Not all Fords have all 5 columns of stability control system data, if there are less than 5 columns the data must be pasted into the appropriate columns. The template is posted at www.ruthconsulting.com.

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ummary 1. Using the “stability control longitudinal acceleration” provided in the example 2012 Lincoln MKZ EDR, it was possible to calculate the reduction in speed of the vehicle between when the g’s first began to increase (assumed to be brake application) and impact. A calculation was made for each interval and accumulated to get the total speed reduction of 4.1 to 4.5 mph, and when subtracted from the last steady state speed yields a relatively precise calculation of speed at impact of 51.7 to 56.7 mph. This calculation is not dependent on the timing of the last reported pre-crash speed value, eliminating a potentially significant source of error. Some reporting delays in the pre-crash speed data have been observed in the early Autoliv RC6 ACM’s, resulting in calculating slightly higher than actual speeds at impact. In this case speed at impact from the pre-crash speed data at 0.5 second

2. Using the speeds above times time, the distance covered along the x-axis of the vehicle between first effective vehicle braking and impact was calculated as 82.3 to 90.3 feet.

3. Using the “stability control lateral acceleration” information, it was possible to calculate the lateral velocity of the vehicle for each interval which accumulated to 3.70 to 3.89 ft/sec at impact. Using the lateral velocity above times time, the lateral distance moved along the y-axis of the vehicle was calculated at 1.5 to 1.9 feet.

This calculation assumes the vehicle was going straight prior to the start of the calculation. Yaw rate can also be used to estimate the lateral distance, but due to slip angle, a higher value of 1.9 to 2.4 feet is obtained.

This empirical data suggests multiplying values obtained from yaw rate alone (for example, from Chrysler vehicles) by a Slip angle correction factor of 0.77.

Realizing this correction factor is based only on this one case, and there could be significant vehicle operational conditions and model to model variations, the authors suggest more research should be conducted on a larger sample size and to get a more robust correction factor.

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intervals was 53.7 to 60.3 mph, the calculation from the stability control system should be more accurate and indeed indicates a lower speed at impact.

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4. Using the arc sine of incremental side movement from lateral acceleration data in the last 0.1 seconds before impact, divided by incremental forward movement, the change in approach angle was calculated as 2.66 degrees. Change in heading can also be calculated from yaw rate and used to estimate change in approach angle, but due to slip angle a higher value of 3.25 degrees is obtained. This empirical data suggests reconstructionists may need to apply a slip angle correction factor of 0.82 times the calculated heading change obtained from yaw rate alone (for example, from Chrysler vehicles) to estimate approach angle change in sudden swerves. Realizing that this correction factor is based on a single case, and there could be significant vehicle operational conditions differences and model to model variations, the authors suggest more research on a larger sample size should be conducted to get a more robust correction factor.

To reduce repetitive effort and promote consistency in calculation, a template has been posted to assist reconstructionists in using this data. The methodology requires a

judgment on when to start the calculation; the clearer the inflection point of the swerve, the more accurately users can select a start point for the calculation.

To date this methodology has only been used in last minute swerves. If the heading changes become large, including where the rear tires begin tracking outside the front (critical speed yaw) or side-slipping, the lateral sensor orientation is changing from the original “Y” direction and accuracy may be reduced. Uses of the calculations include determining if there has been no response to a developing situation versus some response, and calculating the distance of the response to impact, and magnitude of the vehicle response prior to a crash. Determining change in approach angle should improve accuracy of approach angles that may be used for momentum calculations.

The authors remind analysts that the EDR information should be considered an integral part of any reconstruction but must be used in conjunction with all available physical evidence to evaluate the applicability of the recovered data.

12th Annual CDR User's Summit March 6-8, 2017 The CDR Summit is a comprehensive training summit to further the knowledge of those who use the Bosch CDR Tool or who are involved with the collection and analysis of vehicle crash data. This summit covers user applications, case studies, legal issues, legislation and future development of CDR and EDR data collection.

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R R E E

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WE E I V

How Accurate are Witness Distance Estimates Given in Car Lengths?

I

ntroduction Accident reconstructionists generally agree that eye witness estimates of distance are often inaccurate. Robins [2001], for instance, observes that “in spite of the many converging cues available to humans to judge the separation distances and speeds of other objects, we perform poorly at such tasks.” Similarly, Olson [2003] observes that “witnesses’ judgements of distance in the usual measuring units are likely to be unreliable.”

Despite the limitations of humans to judge distance, witnesses and drivers involved in car accidents are often asked to use the unit of a “car length” to estimate how far they were from another vehicle at some point prior to a collision. There seems to be some presumption that reformulating the

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question about distance in terms of “car lengths” somehow improves a witnesses’ or drivers’ ability to judge distance. Law enforcement officers and attorneys, in turn, sometimes use a witnesses’ or drivers’ distance estimates in terms of car lengths to reach conclusions about why an accident occurred or to evaluate whether or not a driver should have been able to avoid an accident.

Accident reconstructionists also sometimes use these distance estimates to perform calculations related to following distance or accident avoidance. www.collisionpublishing.com

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Nathan Rose Neal Carter

John Kreisher Martin Randolph William Neale David Danaher Kineticorp, LLC

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This study examines the validity of using driver and witness distance estimates – given in car lengths – to perform accident reconstruction calculations or to make judgments about a driver’s behavior leading up to a crash. Clearly, the validity of using these distance estimates depends on the accuracy of the estimates, and so, for this study we set up a test to determine their accuracy.

Prior studies by Strauss [2009 and 2010] examined the accuracy of subject’s distance estimates from a stationary vantage point. He found that “the subjects…tended to underestimate distances to objects…the average estimation error was -8.6% while the median error was -22%. The variation in performance among individuals was extremely large, with extreme errors ranging from -96% to +811%.”

Strauss also found that police officers did not differ from the rest of the population in their ability to accurately estimate distances. The study described here differs from the Strauss study in that it examines distance estimates given by drivers in a moving vehicle (i.e., not from a stationary vantage point) estimating their distance behind another moving vehicle.

T

est Methodology This study utilized 20 volunteer test subjects who were asked to drive their own vehicle through a standard and predetermined route and, in the course of the drive, to estimate their distance from a vehicle driving ahead of them in an adjacent lane. One of the study authors rode along with the test subject through the route and posed a series of standard and predetermined questions to the test subject. A second vehicle, which was driven by another one of the authors, was utilized as the lead vehicle to which the test subject would estimate their distance. The use of this second vehicle made the process more controllable.

Figure 1 depicts an aerial photograph that shows the driving route that was utilized for testing with all 20 subjects.

This route, which was located in Greenwood Village, Colorado, consisted of driving on a mixture of highway and surface streets. The portion of the route depicted in cyan was the initial portion of the drive during which the subjects were not asked to make any distance estimates.

The portion of the route depicted in red was the highway driving portion (on Interstate 25) during which the test subjects were asked to make distance estimates while driving at speeds around 60 mph. The portion of the route depicted in blue was the surface street portion during which test subjects were asked to make distance estimates while they were traveling at speeds around 40 mph. This total route distance was 7.6 miles. The tests were all performed between 9:00 AM and 4:00 PM, to allow the subjects to 78 Collision Magazine - Volume 11 Issue 1

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Figure 1: Test Route safely attain a speed of 60 mph without being hindered by heavy commuter traffic.

While driving this route, each test subject was asked to make 5 estimates of their distance behind the lead vehicle.

Four of these estimates occurred on the highway portion of the route and the fifth occurred on the surface street. This resulted in a total of 100 estimates of following distance in terms of car lengths. A sixth estimate was also elicited from the test subjects while they were still seated in the car, but after the route had been completed and the car was stationary.

At the beginning of testing with each subject, the subject was asked for their current age, the age at which they obtained their driver’s license, and their occupation. During

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the highway driving with each test subject, the lead vehicle maintained a relatively constant speed (57 to 60 mph) and the test subject drove behind the lead vehicle, in the adjacent lane to the left. This lane offset was chosen so that a range of distances could be evaluated, without having the test subject directly behind a vehicle at close distances. At four different points along the highway driving portion, the researcher who was riding along with the test subject asked them to verbalize their current speed (read off of the speedometer) and then to estimate their distance behind the lead vehicle, in units of car lengths. As they gave their distance estimate, the researcher documented their estimate and also recorded the actual distance to the lead vehicle, in units of feet. The actual distance was measured using a handheld laser rangefinder with an optic sight (TruPulse 200), as shown in Figure 2. The manufacturer of this rangefinder indicates an accuracy of ±1 foot and a range of 3,280 feet.

Prior to the start of the tests, the rangefinder was tested by taking distance measurements on the highway both through the windshield glass and outside the side window.

Based on this testing, we concluded that the windshield did not affect the measurement, and so, during the testing with the test subjects, measurements were taken with the rangefinder positioned inside the vehicle with the laser projecting through the front windshield. Figure 3 depicts a measurement being taken with the rangefinder. For the purpose of this study, the actual distance was the distance from the observer to the lead vehicle, not the distance from the front bumper of one vehicle to the rear bumper of the other vehicle.

Test subjects were not offered any definition of the term “car lengths” nor were they provided any instructions around how they should define that term. Also, the subjects were not given any feedback about their estimates and they were never told the actual following distances measured by the rangefinder. This was to ensure that their estimates would not evolve due to feedback during the testing.

Once the driving was completed, each subject was asked to state what they meant by a car length in whatever measurement units they preferred. This was done at the end of the test so as to not introduce computation into the subject’s estimates.

Of the 20 test subjects, 13 were female and 7 were male.

The ages of the test subjects ranged from 24 to 62 years old. Driving experience of the test subjects ranged from 8 to 44 years. Professions of the test subjects included retail manager, welder, accountant, mover, dental assistant, stay at home mom, travel agent, delivery driver, information technology professional, and executive assistant.

Figure 2: TruPulse 200 Rangefinder

Figure 3: Measurement of Actual Distance

R

esults For the purpose of analyzing the test results, we chose to define an “actual” car length as 16.5 feet, based on an analysis of passenger car lengths in the Canadian Vehicle Specifications database. This database contains vehicle dimensions and is compiled annually by the Collision Investigation and Research Division of Transport Canada. Our ultimate results and conclusions are not sensitive to this definition, however. The bar graph of Figure 4 shows data for all 100 estimates of distance given by the test subjects during their drives. Data for each test subject is shown separately with the test subjects numbered 1 through 20 on the vertical axis. Each individual estimate by each individual subject is given its own bar on the graph. The horizontal axis shows the ratio between the actual measurement (converted to car lengths using a standard car length of 16.5 feet) and the subjects estimate.

This means that if the subject overestimated the distance, this ratio would be less than 1 and, if the subject underestimated the distance, the ratio would be greater than 1.

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As Figure 4 shows, in only 3 instances was this ratio less than or equal to 1. This means that in only 3 instances out of 100 did the subjects overestimate or accurately estimate the number of car lengths. In 97 out of 100 instances, the subjects underestimated the distance. The actual distances were as much as 6 times longer than what the subjects estimated. The average ratio of actual distance to estimated distance was 2.27, with a standard deviation of 0.96.

Figure 4 also allows for analysis of how the accuracy of the subjects’ estimates evolved through the course of their 5 estimates. There is no obvious pattern to how the estimates progressed for each subject. Some of the subject’s estimates were relatively stable in their level of error. Some of the subjects’ estimates generally improved through the course of the testing. Other subjects’ estimates generally worsened.

Figure 5 shows the same set of data as Figure 4, this time in the form of a histogram. The most frequent estimation error (35 instances) was for the actual distance to be between 1.5 and 2 times larger than what the subjects estimated.

More than half of the distance estimates (51 instances)

were underestimated by more than a factor of 2. That means that based on this data set, if a witness estimated they were 3 car lengths behind another vehicle, there is a more than 50 percent probability that there were actually more than 6 car lengths between their vehicle and the other. The cumulative probabilities were as follows: 97% of the estimates were shorter than the actual distance; 86% of the actual distances were 1.5 or more times greater than the estimate; 51% of the actual distances were 2 or more times greater than the estimate; 31% of the actual distances were 2.5 or more times greater than the estimate; 21% of the actual distances were 3 or more times greater than the estimate; and 12% of the actual distances were 3.5 or more greater than the estimate.

Figures 6 through 10 show the actual magnitudes of the estimates given by the test subjects side by side with the individual actual distances. The actual distances in these graphs are shown in car length calculated assuming a 16.5foot car length. Figure 6 contains the first estimate by each subject, Figure 7 the second, Figure 8 the third, Figure 9 the fourth, and Figure 10 the fifth. Each subject’s estimate

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Figure 5: Histogram Showing Frequency of Distance Estimation Errors is shown with a blue bar and the actual distance is shown with a light gray bar.

These graphs show that the great majority of the estimates given by the test subjects were overestimates. One could reasonably ask the degree to which this outcome is dependent on our definition of “car length.” So far, this article has not addressed the unit of car lengths as it was defined by the test subjects. After each subject was asked to estimate distances, they were asked to estimate the length of a car. Figure 11 depicts the length estimate for each subject. The estimates ranged from a minimum of 5 feet to a maximum of 45 feet, with an average of 14.5 feet and a standard deviation of 9.1 feet. Figures 12 through 16 again show the actual magnitudes of the estimates given by the test subjects side by side with the individual actual distances. This time, though, the actual distances, in car lengths, were calculated using each subjects stated definition of a car length. Figure 12 contains the first estimate by each subject, Figure 13 the second, Figure 14 the third, Figure 15 the fourth, and Figure 16 the fifth. Each subject’s estimate is again shown with a blue bar and this time the “actual” distance is shown with a light blue bar. These graphs show that, even when the subjects set their own definition of the unit of car lengths, in the majority of cases they still significantly underestimate the actual distances. In many cases their estimates of the distance are worse in comparison to their own definition of car lengths than they are in comparison to our more objective definition of this unit. www.collisionpublishing.com

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... even when the subjects set their own definition of the unit of car lengths, in the majority of cases they still significantly underestimate the actual distances.

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Figure 6: Comparison of Subjects’ First Estimate to the Actual Separation Distance

Figure 7: Comparison of Subjects’ Second Estimate to the Actual Separation Distance 82 Collision Magazine - Volume 11 Issue 1

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Figure 8: Comparison of Subjects’ Third Estimate to the Actual Separation Distance

Figure 9: Comparison of Subjects’ Fourth Estimate to the Actual Separation Distance www.collisionpublishing.com

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Figure 10: Comparison of Subjects’ Fifth Estimate to the Actual Separation Distance

Figure 11: Estimate of a Car Length for Each Subject 84 Collision Magazine - Volume 11 Issue 1

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Figure 12: Comparison of Subjects’ First Estimate to the Separation Distance (Subject-Defined Car Length)

Figure 13: Comparison of Subjects’ Second Estimate to the Separation Distance (Subject-Defined Car Length) www.collisionpublishing.com

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Figure 14: Comparison of Subjects’ Third Estimate to the Separation Distance (Subject-Defined Car Length)

Figure 15: Comparison of Subjects’ Fourth Estimate to the Separation Distance (Subject-Defined Car Length) 86 Collision Magazine - Volume 11 Issue 1

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Figure 16: Comparison of Subjects’ Fifth Estimate to the Separation Distance (Subject-Defined Car Length)

Figure 17 shows the same set of data as Figures 12 to 16 in the form of a histogram.

The most frequent estimation error (19 instances) was for the actual distance, as defined by the subject’s estimate of car lengths, to be between 2.5 and 3 times larger than what the subjects estimated. More than half of the distance estimates (62 instances) were underestimated by more than a factor of 2.5, when calculating the actual distance with a car length as estimated by the subject. The cumulative probabilities were as follows: 100% of the estimates were shorter than the actual distance; 90% of the actual distances were 1.5 or more times greater than the estimate; 72% of the actual distances were 2 or more times greater than the estimate; 62% of the actual distances were 2.5 or more times greater than the estimate; 43% of the actual distances were 3 or more times greater than the estimate; 30% of

Figure 17: Histogram Showing Frequency of Distance Estimation Errors

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the actual distances were 3.5 or more times greater than the estimate; 18% of the actual distances were 4 or more times greater than the estimate; and 17% of the actual distances were 4.5 or more times greater than the estimate.

D

iscussion In this study, when a car length was defined as 16.5 feet, 97% of the distance estimates given by the test subjects were underestimates. When a car length was defined by each subject’s estimate of the length of a car, 100% of the distance estimates given by the test subjects in units of car lengths were underestimates. In many instances, the distance estimates were significantly different than the actual distances. This suggests that when witnesses and drivers involved in car accidents are asked to recall and estimate how far they were (in car lengths) from another vehicle, accident reconstructionists and lawyers can reasonably assume that the estimate is inaccurate. Because of that, the use of such estimates should be accompanied with consideration of the ranges reported in this paper.

This finding should not be interpreted as justification for dismissing a witness’s description of an accident altogether.

In our view, a distinction should be drawn between the story a witness tells and the quantitative information they give. Even within the category of quantitative information, reconstructionists should consider what quantitative information may be accurate. For instance, if a witness estimates the speed of another vehicle that they were following behind before the accident, this estimate may be very accurate, since the witness has a device in their car for measuring their own speed (i.e., a speedometer).

The drivers in this study were allowed to focus on the lead vehicle and evaluate the distance before responding with an estimate of the following distance. Witnesses to a crash, on the other hand, are often required to make these estimates based on their recollections of vehicle positions before the crash. This study has examined the subjects ability to estimate their distance from another vehicle in car lengths, but has not tested their ability to recall these distances later.

C

onclusions 1. When a car length was defined as 16.5 feet:

86% of the actual distances were 1.5 or more times greater than the estimate; 51% of the actual distances were 2 or more times greater than the estimate; 31% of the actual distances were 2.5 or more times greater than the estimate; 21% of the actual distances were 3 or more times greater than the estimate; and 12% of the actual distances were 3.5 or more greater than the estimate.

2. When a car length was defined by the estimated length from each test subject: a. In all 100 instances, the subjects underestimated their distance behind the lead vehicle. The actual distances were as much as 8.75 times longer than what the subjects estimated. b. In terms of cumulative probabilities, 100% of the test subjects’ estimates were shorter than the actual distance; 90% of the actual distances were 1.5 or more times greater than the estimate; 72% of the actual distances were 2 or more times greater than the estimate; 62% of the actual distances were 2.5 or more times greater than the estimate; 43% of the actual distances were 3 or more times greater than the estimate; 30% of the actual distances were 3.5 or more times greater than the estimate; 18% of the actual distances were 4 or more times greater than the estimate; and 17% of the actual distances were 4.5 or more times greater than the estimate.

3. Allowing test subjects to give their own definition of the unit of car lengths generally did not improve the accuracy of their estimates. In fact, in many instances, this made the estimates worse.

R 2003.

eferences 1. Olson, Paul L., Farber, Eugene, Forensic Aspects of Driver Perception and Response, Second Edition, Lawyers and Judges Publishing Company,

2. Robins, Patrick J., Eyewitness Reliability in Motor Vehicle Accident Reconstruction and Litigation, Lawyers and Judges Publishing Company, 2001.

3. Strauss, M. and Carnahan, J., “Observed Errors in Disa. In 97 out of 100 instances, the subjects underes- tance Estimation,” SAE Technical Paper 2010-01-0046, timated their distance behind the lead vehicle. The 2010, doi:10.4271/2010-01-0046. actual distances were as much as 6.36 times longer than what the subjects estimated.

4. Strauss M., and Carnahan, J., “Distance Estimation Error in a Roadway Setting,” The Police Journal, 2009. b. In terms of cumulative probabilities, 97% of the test subjects’ estimates were shorter than the actual distance; 88 Collision Magazine - Volume 11 Issue 1

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Release History of Bosch Crash Data Retrieval Tool Software ... or why it’s important to use the latest version of the CDR Tool Software W. R. Rusty Haight Collision Safety Institute

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irst, a little background The Vetronix Corporation was a Santa Barbara, CA based company involved in both the automotive original equipment manufacturer (OEM) and aftermarket segments of the automotive service industry. As a function of a then longstanding relationship with General Motors related to the development and manufacture of diagnostic scan tools 2 (i.e.: the “Tech 1” and later the “Tech 2” 3) Vetronix and GM set out to develop the first Event Data Retrieval Unit (EDRU) in about 1990. That proprietary system incorporated a Vetronix Tech 1 scan tool to retrieve event (crash) data through a vehicle’s diagnostic link connector (DLC) (Figure 2). The retrieved data, presented in hexadecimal format, was then viewable on the Tech 1’s screen or could be printed on the tool’s printer. The initial printed hexadecimal output looking like a wide cash register receipt would then need to be manually interpreted/translated.

Vetronix, again in cooperation with GM, adapted the EDRU concept to a PC based system designed to access and retrieve data stored in an airbag control module in what, in 1999-2000, became the Crash Data Retrieval Tool.

The Vetronix Corp was acquired by Robert Bosch, LLC in 2003 and was initially operated under the functional control of ETAS, a Bosch subsidiary. What was then Vetronix has since been part of the Bosch Diagnostics component of the Bosch Automotive Aftermarket Division as, over time, traces of the Vetronix name slowly faded from the Tool.

For the first three years (2000-2003), the CDR Tool was limited to access to data from GM line vehicles. During that period, Vetronix worked hard to convince other manufacturers who were then just planning to or starting to incorporate event data recording in various components in their vehicles to join GM in allowing for data in their passenger cars, light trucks, and SUVs to be retrievable using the CDR Tool. In February 2003, limited support for Ford vehicles was added to the CDR Tool’s capabilities (version 2.0, February 2003).

In the first three years before Ford vehicle support, there were at least 23 publicly released versions2000, of thewhat CDR Tool version 1.xversion series).1.0.0 Thatofnumber n February then thesoftware Vetronix(the Corp. released represents almost 25% of all publicly versions of the CDR Tool software over the Crash Data Retrieval (CDR)released Tool software (and associated hardware) the full 16 year run. allowing, for the first time ever, public access to crash data stored in Air-

I

Control (ACMs) found inthere passenger cars,changes light trucks Withbag every release Modules of the CDR Tool software, have been to theand program.

SUVs in North America.

A truly ground breaking move, the introduction These changes ranged from the basic (i.e.: the addition of newly supported vehicles) of the CDR marked the a journey now spanning toand the release more complex (i.e.: Tool the addition of a start new of system type by a manufacturer or 1 and providing crash researchers, reconstructionists and 16-plus years functionality of the program’s graphical user interface (GUI)). The first 8others years of the withexistence access to saw dataover associated withreleases crashesof and related events ultimately tool’s 40 public theother CDR Tool software - nearly half of covering more than 90% of the vehicles on the road in North America all the public releases to date - while only two manufacturer’s vehicles wereprothen supduced(GM sinceand 2010 or, by some measures, even earlier. ported Ford).

Over the Tool’sand 16accepting year history, havechange been more than 95 public Understanding the there fact that is an inevitable part ofversion anything asreleases and patches of the software, initially distributed on CD and then - is a sociated with computers - not to mention an ever-evolving automotive industry later by waycomponent of internet download. original release isand dated February4 21, fundamental of using theThe CDR Tool properly effectively.

2000 (Figure 1). As of this writing, the latest release (version 16.6) is dated 5 May hange 31, 2016.

To point out the number of changes to/versions of the software component of the CDR Tool is not meant to suggest there was something “wrong” with the Tool or software, rather, it illustrates both how flexible and capable the

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One has to only think back to childhood and the game “telephone” or “pass the message” 7 to realize that communication between a string of individuals is, at even a very basic level, complicated. Applied here to this technology, imagine a scenario where an OE works with the supplier of an ACM to set up that ACM for use in the OE’s vehicle(s). The specifications applied to the design of the ACM become, in part, the information (often creating another specification) yet another group within the OE’s organization would use to explain to Vetronix/Bosch both how to retrieve data from the module as well as how to translate the retrieved data into a “plain language” report an end user would ultimately see as a “CDR Report.” Like the game “telephone,” passing information from one individual, department and organization to another not only in one direction but sometimes back-and-forth between those groups can surely be expected to result in the occasional miscommunication and perhaps cumulative error.

Figure 1: Start-up screen for the original CDR software release (February 21, 2000)

In the larger sense, the number of releases highlights Vetronix’ (and later Bosch’s) dedication to working with the OEs and relying on input from end users to address the result of miscommunication or error in the most timely way possible. It is perhaps largely for this reason we find the “Important Notice” on the top of the very first page of every CDR Tool report: “… IMPORTANT NOTICE: Robert Bosch LLC and the manufacturers whose vehicles are accessible using the CDR System urge end users to use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting any retrieved data from within the CDR program. Using the latest version of the CDR software is the best way to ensure that retrieved data has been translated using the most current information provided by the manufacturers of the vehicles supported by this product. …”

Figure 2: The first Event Data Retrieval Unit (EDRU) programmer 6 was (is) and points to a recognition by the involved OEs and Vetronix (later Bosch) that reliability of this technology has always been important to a variety of end users and “getting it right the first time” is sometimes harder than it seems in an industry with so many components and perhaps even what may seem to be competing interests.

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The “Important Notice” was introduced and positioned on the top of the first page of the CDR Report with version 3.1 of the CDR Tool software (2008) and the verbiage later changed from Bosch “...recommends...” to Bosch “... and the manufacturers whose vehicles are accessible using the CDR System urge...” in version 3.4 (2009).

The Important Notice is a recognition by everyone involved - from Bosch to the OEs to even end users - that it is inevitable that there will be “issues” with the software that will have to be fixed or changed in later versions of the software and that the best practice is (and always has been) to use the latest version of the software to have the best, most current translation of the retrieved data available to the end user.

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Figure 3: CDR File Information and Deployment Command Data for a 2012 Dodge Charger in version 16.6

Failing to keep current with the software, failing to use the latest version of the software to open and translate a retrieved set of data and then applying that data to a situationally complete analysis 4,8 will inevitably result in, at least, a misrepresentation of the data.

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es, This Really Happened After a crash involving a 2012 Dodge Charger, data was retrieved using the latest version of the CDR Tool software at the time indicating there was a Deployment event. From a CDR Tool report created using the current version for this publication, we see the “Event(s) Recovered” identified as a “Most Recent Event, Deployment” and then, in the “Deployment Command Data” data table, we find both “yes” and a deployment time reported for the deployment of various devices (Figure 3). In Figure 4, we see the driver airbag and driver’s knee airbag deployed; what can be seen in Figure 4 is, of course, consistent with what’s seen in Figure 3. But that’s how the data appears today.

Figure 4: driver airbag and driver’s knee airbag deployed

At the time of the crash, the investigating officer properly retrieved the data with the then current CDR Tool software version 8.1, created a PDF of the CDR Tool report and put that PDF in the case file. The PDF, of course, included on page 1 the “Important Notice” admonition as well as the “File Information Data Table” and “Deployment Command Data” data tables seen in Figure 5.

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Figure 6: CDR File Information and Deployment Command Data for a 2012 Dodge Charger in version 8.1 From the start, the apparent focus in the case was on the speed of the Charger pre-crash (about 92 mph in a 40 mph speed zone); however, the defense attorney had a different focus. The defense attorney was looking at the “Deployment Command Data” data table where, in Figure 5, we find “NO” for the deployment of the driver airbag, “YES” for the deployment of the driver knee airbag and a time for the deployment of both of those devices. It’s not difficult to see that the data reported in Figure 5 is in at least superficial “conflict” with the actual conditions found in the photo (Figure 4) and how that may have caught his attention. The defense attorney was looking to try to demonstrate the flawed presumption that “if there’s an error in the data, the whole thing is unreliable.”

While that position is, on its face, illogical, it’s not surprising that the defense attorney would attempt that approach.

It’s clear that the translation of the data for the indication of whether or not the driver (and passenger) airbags were deployed is incorrect in version 8.1; however, the “yes” or “no” answer is not the only element in the data table to be considered and, clearly, the remaining elements, not to mention the observation one can make of the airbags in the car, tell a more complete story. Focusing on one element in a set of crash facts (or in a CDR Tool report) - whether it’s 94 Collision Magazine - Volume 11 Issue 1

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a pre-crash speed or, here, an inconsistent translation - is to work in a vacuum to the exclusion of all the remaining available information 4,8 and that’s never been a good idea.

Clearly, in version 8.1 there was an incorrect translation of 1 of 3 related elements reflected in the report. But the real failing in this case was that the officer never went back to the original CDRx file; ultimately failing to “...use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting any retrieved data from within the CDR program. ...”

The original data retrieval was followed by pre-trial and then trial testimony and each time the officer testified, he relied on the original translation report created in version 8.1 of the CDR Tool software. However, at the time of the pre-trial testimony, version 14.1 was the current version and the erroneous “no” translation had since been corrected to “yes.” Later, prior to trial, the current version of the software was version 16.2 and what had been “no” in version 8.1 - at that point nearly 3 years out of date - had, of course, long since been corrected to “yes.”

Had the current version of the software been used pre-trial and then at trial, it would have taken the mistranslation issue away from the defense attorney; however, one might

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argue that the lawyer would have simply changed his approach to “well, if that changed, how do we know something else won’t change? Will the pre-crash speed change?

How can we rely on this ‘black box data’ either way?”

tent to use and properly apply CDR Tool technology and might be offered to testify as an expert in that regard, the’s no doubt he should have known moreover, he should have kept current. Which brings about two questions:

The first response should be to point to the Important Notice and to reinforce that the best practice is to use and rely on the latest translation of the data and note how it is obviously correct in the latest translation and had been corrected for a long time before trial.9 One might even contemplate pointing to how the data was translated from the same original hexadecimal data and that hadn’t changed.

The follow up would be to point to the fact that two of the three related elements were originally - and throughout the entire process remained - consistent with the observations in the car. The conclusion was obviously that one element was incorrectly translated, the error was caught and corrected which, going back to the original thought, is at least part of the underlying idea behind the “Important Notice.” Finally, one would have pointed to the idea that the pre-crash data, including the pre-crash speed, was the same across the various versions and when that was compared to other analytical methods ... but that was the next problem in this case: no one did anything else to calculate or estimate the Charger’s speed; it was all about the CDR report data.

1. How can I know what the latest version of the software is?

Ultimately, the prosecution withdrew the CDR Tool translation report because the officer couldn’t explain the “no” entry (never looking at the later translations) and had done nothing to otherwise establish the vehicle’s speed at or before impact. The case against the defendant, where the other driver was killed, was dismissed by the judge with: “… nowhere did this police officer testify as to the direct speed that (the defendant’s car) was doing at the time of the crash. … With that said, this Court finds the defendant not guilty. …”

While it’s clear that there should have been an analysis to go with the data or the data should have gone with an analysis and, either way, that wasn’t done, that’s a topic for another discussion.4,8 Here, we can address changes to the CDR Tool software and, hopefully, reinforce the idea that one should always be working with the latest version of the software (of course while using the data in a situationally complete analysis).

In the case above, one might wonder if the officer ever knew about the later versions of the software. Setting aside the wholly absent underlying connective crash analysis and the failing caused by a focus on one data element or set (here the pre-crash data) to the exclusion of the remainder of the translation report in preparation for testimony not once but twice, if one were to offer themselves as compe-

2. How do I find out about changes between versions?

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nd the Latest Version is...

Unlike typical consumer computer programs, the current version of the CDR Tool doesn’t have a built in “check for updates” feature; the CDR Tool software doesn’t automatically check for updates. Updates have been largely a function of notification issued by Bosch but it is ultimately the responsibility of the end user to follow up on notification, complete the installation and read the “release notes.”

The first part of that process has been such that Bosch introduced the activation certificate with version 3.6 and developed a practice of reaching out to those with current subscriptions by email when each version or patch is released letting them know there was a new release and, when necessary, forwarding a new activation certificate.

That process breaks down when the recipient of the email isn’t actually the end user (i.e.: a government or company purchasing agent not the end user of the software) so the end user never gets the notification or when the email is simply ignored by the recipient or even, as odd as it seems, the end user.

The obvious solution then would be to ensure the end user is the recipient of that email although sometimes, depending on the organization, that’s easier said than done. Ultimately, since the responsibility will lie with the end user, one way to find out what the current version is would be through the Crash Data Group web site 10 from time-totime where the current software version will be available for download and information about subscriptions may be found. The Crash Data Group can also be contacted to determine who the activation certificate email is sent to and make sure it’s being forwarded to the right recipient.

Another resource is found at CrashForum.info 11 where, in the “Crash Data Retrieval User Forum” one would find the topic “CDR Software Version History/Current Version.”

There, one may find the major version release history list and the number of the current version Figure 6.

But the end user’s responsibility doesn’t stop as a function of just having the latest version of the software. That latest version has to be installed and then the latest version ap-

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plied as a case or analysis moves forward and, often, one has to consider there may have been changes to the translation report which might have an impact on the application of the data. That may mean finding out about changes from one version to the next.

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hanges between versions With each new version or patch, Vetronix then Bosch included a section in the Help file within the program called a “What’s new in this Version” (Figure 7) section and, for some, the less well known “Release Notes” as a document found in the installation directory/ folder.

The “What’s New” and/or release notes lay out changes made since the last release. Some notes are significantly more detailed than others. In some cases, the “What’s New” section is a short summary of the changes and the larger “Release Notes” document may contain far more detailed and complete information. In all cases, it is the responsibility of the end user to (1) review the “What’s New” and “Release Notes” in each new version they install and (2) be sure to “...use the latest production release of the Crash Data Retrieval system software when viewing, printing or exporting any retrieved data from within the CDR program. ...” in their situationally complete analysis.

In 2013, George Hall created the thread on CrashForum. info with the subject line “CDR Version History with Significant Changes” where he offered as a periodically updated PDF document the version history and release notes for many of the publically released versions of the CDR Tool software going back to version 1.0.

That thread and his diligent follow up in that regard were an offshoot of his earlier presentation at the first CDR User’s Summit “Practical Aspects of Crash Data Retrieval Using the Vetronix (Bosch) System”. 12 His later involvement in a litigated case where the preservation of a CDRx file and its availability for a review of the original data in the latest version of the CDR Tool software became a central focus 13 also highlighted the importance of the “what’s new” section. With respect to that case, the court held that the CDR Tool retrieved data would be excluded because it wasn’t available to be reviewed in the latest version of the CDR Tool software.

So, on the one hand, we have a case where the data was offered but not represented in the most current version of the software and it was withdrawn when it was clear it was ultimately not going to be admitted at trial. And then in the other case we have a situation where the underlying data couldn’t be reviewed in the latest version of the software - the defense wanted to do that - and the data was

Figure 7: Section in the Help file within the program called a “What’s new in this Version” 96 Collision Magazine - Volume 11 Issue 1

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The thread includes select screen capture graphics (i.e.: where the GUI was changed) as well as other significant program developments beyond the “What’s New” and “Release Notes.” For example, long time CDR Tool users would remember that the “5 second pre-crash graph” was dropped from GM data but do you remember when?

How about when was the term “Near-Deployment” dropped from the program as an event name?

The CrashForum.info thread takes advantage of the Forum’s advanced search functions allowing, for example, a user to look for a specific verbiage change or version number and then go to the individual post on that page within that thread.

Figure 8: Every known version back to version 1.0 was installed on a laptop excluded because it couldn’t be viewed in the latest version of the software.

The recurring theme here is viewing the data in the latest version of the CDR Tool software ... always. There are; however, times when it’s useful to know what changed and when and that’s where a version history like that found at CrashForum.info 11 comes in.

As this issue of Collision is being shipped, the original thread created by George Hall (“CDR Version History with Significant Changes”) has been replaced by an expansion of the thread “CDR Software Version History/Current Version” (Figure 6) which will list the current version at the top of the thread and then, in separate posts below that, by release, the “What’s New in this version” text and, where they were made available, the “Release Notes.”

To create the thread posts, a laptop was built with Windows XP installed and then every known version back to version 1.0 was installed on that laptop (Figure 8). Interestingly, that process posed a problem because early versions (at least version 1.0) were written for Windows 95/98 so after a workaround, the installations were complete and the documentation extracted. Although originally designed for Win95/98, the current version of the CDR software supports Windows versions from Windows XP through Windows 10.

CrashForum.info is now the largest publically accessible crash community on line with over 1675 members and is open to those involved in the crash analysis and reconstruction community. Full access to threads and the ability to post to the Forum is limited to verified members to avoid spamming and minimize “trolling.”

Full access is free and verification requires email activation and a reply to the activation information request.

Footnotes: 1. Haight, W. R. Rusty, “An Abbreviated History Of CDR Technology,” Collision Magazine Volume 5, Issue 1, 2010 2. In context, an automotive scan tool is an electronic tool used to interface with, diagnose and even reprogram vehicle control modules. On most vehicles made after 1996, the scan tool is most often connected to the vehicle and vehicle systems through the diagnostic (or data) link connector. Depending on its capabilities, a scan tool enables a mechanic or “do it yourselfer” the ability to read certain diagnostic trouble codes (DTC) and other diagnostic information from systems in a motor vehicle.

3. “... While an off the shelf scanner will read the Engine Diagnostic Trouble Codes (DTCs), the Tech 2 will read and report information from all the car’s systems, including ABS, Air Conditioning, Air Bags, and other systems a standard scanner will not read. ... Vetronix made the Tech 1. The original GM Tech 2 was made by Hewlett Packard and used Vetronix software. Vetronix purchased the Tech 2 manufacturing rights from HP. In 2003, Vetronix was acquired by ETAS, a supplier of standardized development and diagnostic tools for electronic control units.

In 2006, the Vetronix Aftermarket division merged with Bosch Automotive Aftermarket, responsible for supply, sales and logistics of automotive parts for service of the ve-

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hicle. So today the Tech 2 is ‘made’ by Bosch. ... The Tech 2 is used for GM Vehicles from 1992-present.” - Nunnaly, Bruce, 2010, “GM Bosch Vetronix HP Tech 2 with CANDI” retrieved from http://www.caddyinfo.com/wordpress/ gm-bosch-vetronix-hp-tech-2-with-candi/ 4. Haight, W. R. Rusty, “Using CDR System Data in Crash Reconstruction or What Does the Term “Complete Reconstruction” Really Mean?”, Collision Magazine Volume 6, Issue 1, 2011 5. “It is not the strongest of the species that survives nor the most intelligent that survives. It is the one that is most adaptable to change.” Charles Darwin 6. The original programmer and individual who has been involved in the CDR Tool software from the time it was a Vetronix product is Jeff Weger (See also Figure 1).

7. An exercise often used to illustrate cumulative error in communication between individuals.

8. Haight, W. R., “Crash Data Retrieval Data Analyst’s Certification Course, Collision Safety Institute”, 2002 The terms “situationally complete” and “situationally appropriate” analysis first appear in course materials in 2002.

Prior to that time, the concept was described as “(CDR

data) Supplements good investigative and analytical procedures.”

9. “The good news about computers is that they do what you tell them to do. The bad news is that they do what you tell them to do.” - Ted Nelson (Ted Nelson, American pioneer of information technology, philosopher, and sociologist. He coined the terms hypertext and hypermedia in 1963 and published them in 1965.)

10. At this writing the latest version of the CDR Tool software is available at the Crash Data Group website at: http://crashdatagroup.com/software/index.html and the Bosch Diagnostics website at: http://www.boschdiagnostics.com/cdr/ 11. http://www.crashforum.info/index.php 12. Hall, George, P. E.,“Practical Aspects of Crash Data Retrieval Using the Vetronix (Bosch) System” Collision Magazine, Volume 1, Issue 1, 2006 13. Hall, George, P. E.,“CDR Evidence Suppressed by Oklahoma Court” Collision Magazine, Volume 5, Issue 1, 2010

12th Annual CDR User's Summit March 6-8, 2017 The CDR Summit is a comprehensive training summit to further the knowledge of those who use the Bosch CDR Tool or who are involved with the collection and analysis of vehicle crash data. This summit covers user applications, case studies, legal issues, legislation and future development of CDR and EDR data collection.

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tu e S

Cas

Case Problem Solutions

w. R. Rusty Haight

Collision Safety Institute Project: “2005 Pontiac G6” The solutions to this case problem based on a review of the data provided focuses largely on a reading of the Data Limitations and careful reading of the elements found in the report. This case problem and solution reinforce the idea that, to properly apply CDR Tool retrieved data, one has to consider appropriately linked data elements often beyond any one, single element.

1. The data for the recorded Non-Deployment reports a value for “Maximum SDM Recorded Velocity Change (MPH).” There are also data tables for “SDM Longitudinal (and lateral) Axis Recorded Velocity Change (MPH).” and “SDM Lateral Axis Recorded Velocity Change (MPH).” which report the delta-V relative to those axes. Start by comparing the delta-V from the data tables, of course in the context of the Data Limitations, with that reported as the “Maximum SDM Recorded Velocity Change (MPH).”

Are there any apparent “differences?” If so, how might that be resolved?

The question points to the data element “Maximum SDM Recorded Velocity Change (MPH)” found in the “System Status At Non-Deployment” data table (Figure 5) which reports a delta-V of 9.11mph (14.66km/h) and asks: how does that compare to the Data Limitations text relative to this element and the data found in the tables for “SDM Longitudinal Axis Recorded Velocity Change (MPH)” and “SDM Lateral Axis Recorded Velocity Change (MPH)” (Figure 6)?

In Figure 2 (the Data Limitations text), we find these passages regarding delta-V: “... SDM Recorded Vehicle Velocity Change reflects the change in velocity that the sensing system experienced during the recorded portion of the event.

SDM Recorded Vehicle Velocity Change is the change in velocity during the recording time and is not the speed the vehicle was traveling before the event, and is also not the Barrier Equivalent Velocity. ... Velocity Change data is displayed in SAE sign convention. ...

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in velocity. If a CDR Printout user were to calculate resultant velocity change using X and Y axis time history data, the calculated value may be different than the Maximum SDM Recorded Velocity Change parameter value displayed in the CDR report. This is due to the rounding that occurs within the SDM while calculating the Maximum SDM Recorded Velocity Change value. ...”

Next, we should look to Figure 6 for a resultant deltaV. The data tables are labeled “SDM Longitudinal (and lateral) Axis Recorded Velocity Change (MPH)” as referenced in the passage(s) above. The maximum value for delta-V on the Longitudinal axis shows -9.49mph (-15.27km/h) and for the Lateral axis 0.68mph (1.09km/h).

Even a casual observer would notice that, yes, there is a difference between the “Maximum SDM Recorded Velocity Change (MPH)” (reported as 9.11mph) and the total one would calculate for a resultant delta-V using the “SDM Longitudinal (and lateral) Axis Recorded Velocity Change (MPH)” data sets. In that case, the resultant would be 9.49mph (15.27km/h). While that is, of course, the magnitude of the resultant delta-V, the direction (PDOF) would be about -4deg off the longitudinal axis.

Relative then to the issue of polarity, the Data Limitations tells us that “ ... Velocity Change data is displayed in SAE sign convention. ...” Given that the only elements of those discussed which would, by themselves, have indicated polarity would be the “SDM Longitudinal (and lateral) Axis Recorded Velocity Change (MPH)” data sets we may assume that the polarity reference in the Data Limitations is actually specific to these data sets and not the “Maximum SDM Recorded Velocity Change (MPH).” In short then, polarity is not a “difference.”

The magnitude of the calculated resultant delta-V compared to the “Maximum SDM Recorded Velocity Change (MPH)” is; however, a “difference.” The reported value found in the System Status data table (9.11mph) is, according to the Data Limitations text, “...the maximum square root value of the sum of the squares for the vehi-

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cle’s combined “X” and “Y” axis change in velocity. ...”

One is left to wonder then how if the resultant were to be calculated in the same fashion from the data found in Figure 6, it would yield an answer of 9.49mph.

2. There is one recorded event; the Non-Deployment event.

Based on the remainder of the data found in the report from the Pontiac, this module “saw” a total of how many related events?

The Data limitations touches on this point where it reads: “... If a CDR Printout user were to calculate resultant velocity change using X and Y axis time history data, the calculated value may be different than the Maximum SDM Recorded Velocity Change parameter value displayed in the CDR report. This is due to the rounding that occurs within the SDM while calculating the Maximum SDM Recorded Velocity Change value. ...”

The data reports one recorded Non-deployment event (Figure 1) and one “Associated Event Not Recorded” (Figure 3). That “observed” but not recorded event is identified as a “Non-Deployment” which preceded the recorded Non-Deployment (Figure 3).

Clearly it is different and it begs the question: how does rounding the maximum values in the “SDM Longitudinal (and lateral) Axis Recorded Velocity Change (MPH)” data sets result in a number greater than that found in the System Status summary “Maximum SDM Recorded Velocity Change?” Perhaps a clue is found in the verbiage of the Data Limitations text itself where it reads “... This is due to the rounding that occurs within the SDM while calculating the Maximum SDM Recorded Velocity Change value. ...”

Where the “Maximum SDM Recorded Velocity Change” might be calculated then recorded in the SDM, the underlying quantities used (in the SDM) to make the calculation are not recorded and we are left to speculate what they might have been. In contrast, the quantities used to calculate the resultant using the “SDM Longitudinal (and lateral) Axis Recorded Velocity Change (MPH)” data sets may not be subject to the same rounding as was done in the SDM when the single maximum value was recorded.

In the final analysis; however, is the “difference” between these quantities really a significant qualitative concern to the analyst? While opinions may vary, there is obviously a quantitive difference (the difference in the magnitude of the numbers themselves), but if one were reasonably working from a range for the delta-V in the first place (for example, adopting that the magnitude of the delta-V in this case was between 9 and 10mph rather than the arguably over precise 9.11 or even 9.48mph), the magnitude of the difference becomes effectively irrelevant. Undeniably, the difference is one to be aware of but the significance is of virtually no real consequence in the scope of a larger analysis particularly when one is or should be working with a range to start with.

3. Based on the data for the Pontiac as recorded and reported, there was an “observed” but not recorded event which would have had a delta-V of greater than _______ but less than _________ (select units as appropriate) and would have occurred within ______ seconds of the recorded event. How would this other event relate to the overall crash sequence as described?

For the first part of this question, it would be far too easy to look to the Data Limitations text (Figure 2) and suggest that the delta-V of the not recorded but “observed” Not-Deployment would have to be at least 5mph (8km/h) where we find the entry: “... The minimum SDM Recorded Vehicle Velocity Change, that is needed to record a Non-Deployment Event, is five MPH. ...” However, a careful further reading of the entirety of the Data Limitations also tells us that “...

The SDM can store up to one Non-Deployment Event.

This event can be overwritten by an event that has a greater SDM recorded vehicle velocity change. ...”

With this information, if we contemplate the “observed” but not recorded Non-Deployment (setting aside what we might know from the at-scene information associated with the human, vehicle and environment), we know that the not recorded event had to have been, at a minimum, sufficient to “wake up” the module (or the event would not have been “observed” and “associated”) but we should not loose sight of the idea that the requirement described in the Data Limitations calling for a 5 mph delta-V has to do with recording, not simply a “recognition” by the ACM that the event occurred. The ACM has to (1) “see” the event, (2) make a deployment (here a Non-Deployment) decision and then (3) make the record/don’t record decision based on whether or not the Non-Deployment qualifies for recording for that individual event. Here, we see that the delta-V which would qualify the event for recording is 5mph but the end result had to take into account the limit of only one Non-Deployment event ultimately being recorded and that a greater severity, later occurring Non-Deployment would overwrite a previous event.

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In short then, the “observed” but not recorded event which would have had a delta-V of greater than zero mph but less than 9.11 mph. The upper bound would be the lesser of the reported delta-V values discussed in question 1 in this problem series. The “observed” but not recorded Non-Deployment would have had to have had a lesser delta-V than that for the recorded event or the event that ultimately was recorded would have been instead referenced as the “Associated Event Not Recorded” and the report would have indicated that event occurred after the recorded event.

“... The 1.0 second Pre-crash data value (most recent recorded data point) is the data point last sampled before AE. That is to say, the last data point may have been captured just before AE but no more than 1.0 second before AE. All subsequent Pre-crash data values are referenced from this data point. ... Pre-crash data associated with this event will always be for the first event even if it is not recorded. ...”

4. How is the pre-crash data found in the CDR Tool report from the Pontiac associated with the recorded NonDeployment?

able (“AE”) was that for the “observed” but not recorded Non-Deployment and the last pre-crash data set would have been captured up to one second before that event’s “AE” (question 5).

In the System Status at non-Deployment Data Table, we find a parameter which reads: “Vehicle Event Data The reference to the time association or association (Pre-Crash) Associated With This Event” where the with respect to time is less clear from the report. The reference to “this event” is, in context and placement, closest thing we find in the Data Limitations to a time meant to refer to the recorded Non-Deployment. The element between events is the sentence: “The data in translated value reads “no,” meaning the pre-crash data the Non-Deployment Event file will be locked, if the is not associated with the recorded Non-Deployment.

Non-Deployment Event occurred within five seconds So what event is it association with? of a Deployment Event. ...” The word association is not specifically found in the passage although used The Data Limitations reads, specific to this analysis, elsewhere for other elements. However, reading this “... Pre-crash data associated with this event will alreport (and other GM system reports) puts, to some ways be for the first event even if it is not recorded ...” degree, the word “association” in context. It appears and we already know the “observed” but not recorded that, generally, “association” has to do with a period Non-Deployment occurred first. Reasonably then, between events and, here, that period would be 5 sec- the pre-crash data is associated with that “observed” onds (or less). For that reason, one may assume that but not recorded Non-Deployment. the “observed” but not recorded Non-Deployment All the information from the report taken together which occurred before the recorded Non-Deployment tells us that the pre-crash data must belong to the occurred within the 5 seconds before the recorded “observed” but not recorded Non-Deployment so, in event. that regard, the first decision making Algorithm En-

5. Relative to the pre-crash data for the Pontiac, we find a set of data “Pre-Crash” elements marked as relative to time “-1sec” (Figure 4). That set of data would have been captured by the ACM approximately how long (in time) before the ACM began it’s first decision making algorithm in this series?

6. Relative to the pre-crash data for the Pontiac, we find a set of data “Pre-Crash” elements marked as relative to time “-1sec” (Figure 4). That set of data would have been captured by the ACM approximately how long (in time) before the ACM began it’s decision making algorithm for the recorded event’s data set?

The last three questions are actually related and can be answered in one section together. Starting with the Data Limitations text for this case problem, we find these passages:

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Given the notion of association discussed previously and lacking a specified time between the “observed” but not recorded Non-Deployment and the recorded Non-Deployment, but working with a contextual belief that the period creating that association is 5 seconds, the pre-crash data could have been captured as much as almost 6 seconds before “AE” for the recorded Non-Deployment (where there could be up to 5 seconds between the unrecorded Non-Deployment and the recorded Non-Deployment and then up to one second between the “-1" data set and the “AE” for the unrecorded Non-Deployment for a total of “up to about” 6 seconds).

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Project “2010 F150 Raptor” As is often the case when reviewing CDR Tool retrieved data, more than one issue or aspect of the data can be addressed or examined at the same time (as seen in the previous case problem example). Here, if we figure out what the driver was doing leading up to the turn, it helps us understand - at least to some extent - the type of turn he was making or could have been making and ultimately about how much he turned his vehicle from its original path leading up to the crash. Therefore, the first three questions can really be addressed together: 1. Based on the data retrieved from the Raptor as offered in the sections of the CDR Tool report found here as Figures 8-13 and assuming that prior to the pre-crash period found in the data the truck was moving in essentially a straight line toward the intersection, did the driver appear to be turning left turn or right from his original path into the intended turn?

1.a Would the data from the Raptor support the driver making or appearing to make a U-turn (assume on to be legal at this intersection)?

2. Assuming that immediately prior to the pre-crash data period the Raptor was traveling along a straight path, call it a baseline for this analysis. At impact, how many degrees had the Raptor turned from that path and in what direction (i.e.: right or left relative to the driver’s perspective in the truck)?

A look at the information found in the data table “PreCrash Data -5 to 0 sec [10 samples/sec] (First Record)” tells us we can examine the driver’s pre-crash steering

leading to the event in more detail in a couple different ways. For the period marked by time element -5sec down to the -3.2 time element data set, it appears the driver is steering in a “negative” direction (Figure 12) but only by a few degrees. At the -3.1sec time element data set, it appears the driver reverses steering directions and begins steering in a “positive” direction in an application of steering that gets increasingly larger to maximum of about 268 deg.

The question is: what direction does this data tell us he was steering; clockwise or counterclockwise?

To determine that, we’d need to know what the manufacturer means to convey given the polarity of the various data elements and here, in particular, the steering angle. Ultimately, the question is: what is “positive” and what is “negative” steering?

There’s nothing in the Data Limitations (Figure 9) which would clear up the question of polarity for any particular element specifically including the steering angle. While virtually all the other manufacturers whose vehicles are supported for data retrieval using the Bosch CDR Tool provide, in the Data Limitations text, a Data Polarity table (where appropriate) here we don’t find one for the Ford F150 Raptor. Luckily, there is information available to us elsewhere.

In the CDR Tool Help File (Figure 14), we find an entry which would be tracked down by looking (1) first at the 2010 F150's support listing in the Help File, (2) then finding a link there to the type(s) of data which would be retrieved from a module type accessible di-

Figure 14: Bosch CDR Tool Help File www.collisionpublishing.com www.collisionpublishing.com

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rectly using the CDR Tool cable F00K108384 and then (3) working our way down the list of potential data from this module type to the pre-crash data.

There, as seen in Figure 14, we finally find an indication that a positive steering angle is counterclockwise steering.

At this point, what we know reviewing the precrash data and applying the knowledge we have for polarity, is that the driver applied a small amount of clockwise steering then reversed the steering to what appears to be a significant degree in the counterclockwise direction (pun intended). For the first part of this analysis then, it appears the driver is effectively overall making a left turn relative to his original path.

To further evaluate the data and the follow up questions, we can use the CDR Tool’s CSV export function, create a “comma separated values” (CSV) text file and then we can import that file of the pre-crash data elements into a spreadsheet and more fully evaluate the turning movement of the truck. While there is other analysis which may be undertaken using the CSV file, to focus narrowly on the turning movement of the truck leading up to impact, the yaw rate (captured by the truck’s stability control system pre-crash) may be used to determine how far the truck had rotated from its original path/ heading.

As with the steering angle, in the report, the polarity of the yaw rate is otherwise unspecified and left to the user to determine. Even if we look to the same process seen above turning to (1) the 2010 F150's support listing in the Help File, (2) finding a link there to the type(s) of data which would be retrieved from a module type accessible directly using the CDR Tool cable F00K108384 and then (3) working our way down the list of potential data to the pre-crash data to the yaw rate, we don’t find a specified polarity for any elements other than the steering angle. However, given that process provided the steering polarity and, particularly in this instance, it lead us to understand how the driver was turning the truck, we can deduce that the yaw rate polarity is similarly positive for counterclockwise rotation of the vehicle around its center of mass.

In other words, a left turn would result in positive yaw rate data.

As seen in Table 1, using the recorded pre-crash yaw rate data, the truck initially moved very slightly toward the driver’s right and then began a more sig-

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Times (sec) -5 -4.9 -4.8 -4.7 -4.6 -4.5 -4.4 -4.3 -4.2 -4.1 -4 -3.9 -3.8 -3.7 -3.6 -3.5 -3.4 -3.3 -3.2 -3.1 -3 -2.9 -2.8 -2.7 -2.6 -2.5 -2.4 -2.3 -2.2 -2.1 -2 -1.9 -1.8 -1.7 -1.6 -1.5 -1.4 -1.3 -1.2 -1.1 -1 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0

Steering Wheel Angle (deg)

-1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 -1.3 3.2 -1.3 -5.8 -10.3 -10.3 -10.3 -10.3 -5.8 -1.3 3.2 12.2 25.7 43.7 61.7 88.7 115.7 147.2 174.2 196.7 214.7 228.2 241.7 250.7 259.7 264.2 268.7 268.7 264.2 264.2 264.2 259.7 255.2 250.7 246.2 237.2 232.7 228.2 228.2 223.7 210.2 187.7

Stability Control Yaw Rate (deg/sec)

-0.75 -0.87 -0.75 -0.25 -0.87 -0.62 -0.37 -0.37 -0.62 -0.75 -0.62 -0.75 -0.37 -0.87 -0.75 -1.25 -1.37 -0.75 -1.25 -1 -0.75 -0.12 0.37 1.5 3.12 4.37 6.25 8.87 11.37 13.5 15.25 17 19 19.87 21.25 22.5 23.12 23.12 23.5 24.12 25 24.62 24.62 24.62 24.62 24.62 24.75 24.62 25 23.87 22.5

Calculated Yaw Angle (deg)

-0.08 -0.16 -0.24 -0.26 -0.35 -0.41 -0.45 -0.49 -0.55 -0.62 -0.68 -0.76 -0.80 -0.88 -0.96 -1.08 -1.22 -1.30 -1.42 -1.52 -1.60 -1.61 -1.57 -1.42 -1.11 -0.67 -0.05 0.84 1.98 3.33 4.85 6.55 8.45 10.44 12.57 14.82 17.13 19.44 21.79 24.20 26.70 29.16 31.63 34.09 36.55 39.01 41.49 43.95 46.45 48.84 51.09

Table 1: Calculated yaw angle

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nificant turn back to the left. Imagine a driver approaching an intersection with a cross street and entering a left turn channel or left turn lane. At the onset of the turn, the driver might steer slightly right to “clear” a center median strip or move slightly to the right to allow for more room in a followup U-turn which might be considered a potentiality given the initial negative (clockwise steering). On the other hand, the magnitude of the clockwise steering (on the order of less than 2 degrees) is largely meaningless here. That small angle (together with the small amount of steering in the larger sense), with no recorded data for the period immediately before the “-5 sec” period could be the result of a number of driver choices largely unrelated to the turn and should be considered against the crash environment but it doesn’t necessarily indicate a U-turn or any real meaningful steering application by this driver.

In the larger analysis, the remainder of the yaw rate data does lead us to better understand the movement of the truck once in the intersection or the turn itself leading to impact. The steering application (at the pre-crash speeds shown in the pre-crash data at Figure 11) describe an initially slowly developing left turn which, as the driver accelerates into the turn, increases. Finally, near impact, the truck as been redirected to the driver’s left (making a left turn) to about 50 degrees from its original path (question 2).

But could the driver have been making a U-turn (question 1.a)? To more fully answer that question we would have to consider (1) the speed of the truck into and through this turn, (2) where the speed, time and distance in the turn would put the truck relative to (3) the width of the road onto which the U-turn might be made. Clearly, the roadway dimensions were left out of this case problem and that should now highlight the idea that the CDR Tool retrieved data still needs to be considered in the context of the crash facts: the human, vehicle and environment information available to the analyst. For this case problem then, the best answer to 1.a given the information available here would be “maybe, it depends on what we know about the crash scene; the intersection.” At the same time, some of the remaining questions might shed at least a little light on this potential.

3. Based on the data available for the Raptor, what would be the approximate PDOF for the truck (what angle relative to what axis)?

Focusing on the available data for an answer and to the exclusion for this case problem of an analysis of the actual damage on the truck or a momentum based analysis

leading toward a calculation there of PDOF, the Principal Direction of Force (PDOF) can be calculated from the CDR Tool report’s longitudinal and lateral delta-V data. The wrong way to do it would be to simply take the maximum longitudinal and lateral delta-V values found in Figure 10 because those occur at different times in the crash pulse. Instead, with an understanding that the longitudinal delta-V is the larger of the two and, as a result, we’d be measuring the PDOF from the longitudinal axis, we would look to the crash pulse data found in Figure 13.

In Figure 13, we find that, at about 160ms (the basis for that selection being the maximum delta-V on the primary axis involved) the reported longitudinal component of the crash pulse is about -10.71mph (-17.23km/h) and the corresponding component on the lateral axis is -5.74mph (-9.24km/h) at about 160ms. With a recognition that these axes are at a right angle to one another, we can take the arctangent of the opposite over the adjacent (here the opposite is the lateral component over the adjacent longitudinal component) and find that the PDOF would be - from the recorded crash pulse data - approximately 28 degrees (clockwise following the convention found in SAE J211/J1733) measured clockwise from the truck’s longitudinal axis and then applied through the centroid of damage at or around the right rear wheel.

In Figure 7 and from the description of the crash, we know that the impact area on the truck is at the right rear wheel so the application of force on or to the truck, in that area, would be measured at about 28 degrees off the longitudinal axis of the truck while it is in its turn.

The PDOF is shown graphically as an approximation in Figure 15. In Figure 15, to move forward in this analysis as seen below, we include a generic intersection for a frame of reference. All that, of course, leads us to the final follow up question.

4. Given the driving pattern established thus far for the Raptor, can you determine if the smaller passenger car had been originally approaching from the opposite direction the truck had been traveling or on the cross street prior to impact?

To fully and completely address this question one would need (1) the PDOF for the truck (we have that), (2) more information about the damage to and the PDOF for the involved car and (3) the layout of the roadway.

We have data from the truck to work with; however, we have to recognize that we still need information from the vehicles and environment to completely address the question posed. Ultimately, then this case problem might be used to reinforce the idea that we need to ex-

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Figure 15: Ovehead diagram of intersection with 2010 Ford F150 Raptor

Figure 16: Overhead diagram of intersection at impact

amine reported elements and how they might be better understood or applied together with what human, vehicle and environmental information might be available and then a view toward identifying what we would need to fully answer the questions.

(2) The passenger car was not coming from the truck’s left relative to the truck’s original path. Again, the damage location on the truck doesn’t lend itself to a scenario where the passenger car was coming from the truck’s left and managed to strike the turning truck on the right rear acting in a relative front-to-rear direction.

For the moment and solely for this case problem, we might adopt that the given description of the crash lays out damage to the front, primarily the right front, of the car. Further, again to move the solution forward, we may adopt that the intersecting roadways fall at a right angle to one another (as seen in Figure 15).

As we move toward an analysis, we should remember that the PDOF (calculated previously relative to the truck) is not necessarily the same as the two vehicle’s relative heading angles at impact. The PDOF represents the direction of the force acting on the truck and one would need to know something about the PDOF for the involved car to see if or how those relative force lines represent their relative PDOFs within the meaning of “opposite” in the context of “equal and opposite.”

However, if we consider that the truck has moved some 50 degrees from its original path and the force line acting on the truck falls about 28 degrees clockwise off it’s longitudinal axis at the right rear wheel, without knowing too many specifics about the roadway layout it’s still probably reasonable to conclude that...

(1) The involved passenger car was not originally following the truck; it was not driving in the same direction as the truck prior to the crash. Had it been driving the same direction, following the truck, it would have had to have passed the truck and somehow reversed direction to strike the right rear side of the truck acting front-to-rear.

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(3) The passenger car was not coming from the truck’s right relative to the truck’s original path. While the damage to the right side of the truck would suggest this as a possibility, it would mean that the passenger car would have had to somehow make a sweeping right turn then sharp left turn to be able to approach the left turning truck at impact and this falls outside a reasonably probable path.

So that leaves the most reasonable conclusion being that the passenger car was originally traveling in the opposite direction of travel as the truck (before the truck turned) prior to the crash in the opposing lane(s) of travel. Given the angle of the truck to the roadway (having turned to about 50deg from its original path) this further suggests the probability that the passenger car swerved to its left at least slightly prior to the crash.

In summary, the data from the truck gives us a relative abundance of information which - together with the human, vehicle and environment information which would be gathered during a normal crash investigation - would provide the crash analyst with what otherwise might be “missing” or at least imprecise pieces of the crash puzzle.

The case problem would then reinforce the idea that the crash data may be used with a crash analysis, to supplement and “fill in the blanks” in a crash analysis but we still need the human, vehicle and environment information to do the job right.

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