Volume 10, Issue 2

C

2015 Vol.10 Iss.2

ollision C The International Compendium for Crash Research

Volume 10 Issue 2

FAll 2015

Collision: The International Compendium for Crash Research

Reviews of Crash Testing

Occupant Kinematics in Low-Speed Bumper-to-Bumper Rear Impacts

collisionmagazine.com

Volume 10, Issue 2 - FALL 2015

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Contents

Fall 2015

Volume 10 Issue 2

inside

18

06

Letter From the Editor

08

Crash and Learn 2015

10

A Team Approach to Crash Investigation

16

The Rest of the Story

features

36

18

Kinematics of Braced, Un-braced, and Out-of-position Occupants in Lowspeed Bumper-to-bumper Rear Impacts by: Lawrence A. Wilson and Sean Haight

36

My Turn at the Wheel: Don't Look Too Closely by: Erik Carlsson

62

A Review of the Development and Validation of Simulation Technology for Vehicular Accident Reconstruction by: Brian G McHenry

82

Traffic Camera Video Analysis Validation by: Adam Cybanski

62

case study

82

44

Case Problem: Who Hit Whom First? by: W. R. Rusty Haight

102

Case Problem Solution

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Collision Magazine - Volume 10 Issue 2 3

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 4

Collision Magazine - Volume 10 Issue 2

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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, © 2015 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 editor@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

PAGE

4N6XPRT Systems AR Search Engine ARC-CSI Crash Conference ARC Network CDR User's Summit Collision Magazine Collision Safety Institute Crash Data Group EDR Experts Engineering Dynamics FARO MapScenes MapScenes Northwestern CPS Vericom Computers Visual Statement Visual Statement

114-115 98 61 35 42 56 113 99 112 Inside Front 43 2 57-60 81 15 100-101 Back Cover

Included with this issue of Collision

is the 2015 ARC-CSI Crash Conference flash drive.

This flash drive includes all the photos, video, instrumented crash testing data, scaled diagrams, speaker PowerPoints, and all other supporting data in an easy to use flash drive. Over 7 GB of data!

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

BACK ISSUES Next ARC-CSI Crash Conference 15th Anniversary 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

May 23-26, 2016 www.arccsi.com

Collision Magazine - Volume 10 Issue 2 5

Letter from the editor ith this issue we have successfully completed 10 years of publishing Collision Magazine. Reflecting back on the past ten years Collision Publishing is making some changes. Rumors have been circulating about these changes and the reasons for the changes. The truth is we are and we believe these changes will be for the benefit of our loyal subscription base. Our mission at Collision Publishing is to always deliver Collision Magazine to our subscribers with relevant, new research at a fair price.

W

As Collision Magazine goes forward, it is important to provide a little history. The story begins with the magazine that almost wasn't. As Collision Publishing was planning its first CDR User's Summit, co-sponsored by NAPARS, the intent was to publish the Summit proceedings in the Accident Reconstruction Journal (ARJ). As it turned out when the time came to publish the proceedings the ARJ declined. To live up to our promise to the speakers at the 2006 CDR Summit, we decided to publish a new magazine for the industry; hence the birth of Collision Magazine. For the first and subsequent issues, Collision Publishing entered into an agreement with NAPARS such that we would offer Collision Magazine twice a year and NAPARS could make it a benefit of its membership. As with any contract, there were detailed obligations on both sides.

In 2010 and going into our fifth year of publication, a new President was elected to serve the NAPARS organization and with that the contract between Collision Publishing and NAPARS was reviewed, modified and agreed to.

From 2011 through 2015, NAPARS membership levels unfortunately declined and reached a number below the lowest number specified in the contract to where NAPARS benefitted from "favored nation status" with respect to special offers and guaranteed subscription rates. During that time, Collision Publishing always met and even went above and beyond the terms of the contract with NAPARS. For example, despite provisions allowing for a subscription rate increase as the membership

6

Collision Magazine - Volume 10 Issue 2

numbers dropped below specified levels, Collision never took advantage of that provision and, instead, offered time and again to help NAPARS build its membership levels. In contrast, the leadership of NAPARS (the same leadership in place since the contract was renewed in 2010) has failed to meet multiple provisions of the organization's responsibilities under the contract and acted, in many ways, contrary to both the letter and spirit of the agreement. It became clear as the 2015 Combined Conference date approached and with the attention of the organizers of that conference clearly elsewhere, there was going to continue to be a complete lack of responsiveness from the NAPARS leadership, that nothing was going to change, and NAPARS membership and commitment to Collision Magazine would continue to diminish. It was time for Collision Publishing to step up and make a change.

Although recently misrepresented as "abrupt," after this ongoing lack of commitment to the longstanding contract, Collision Publishing informed the NAPARS leadership that we are exercising our option to cancel the contract between NAPARS and Collision Publishing effective October 2015. Given the history, this wasn't "abrupt" and shouldn't have been a surprise to the leadership particularly since lengthy discussion of the many issues which needed to be addressed with the NAPARS leadership (as recently as June 2015) went without action by the NAPARS Board. Collision Publishing had finally reached a point where we had to consider the value of an increasingly one-sided relationship with NAPARS and our responsibility to our subscribers both from the NAPARS member body and beyond.

Although there was another misrepresentation that "no other option to purchase Collision (was) being offered to (NAPARS)," the truth is, with our notice of the cancelled contract, Collision Publishing offered to assist the NAPARS Board to fulfill

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their subscription commitment to its members for 2015 and, for some, into 2016. This offer wasn't required by the contract and wasn't requested by the NAPARS Board and, in fact, they initially balked at the idea. As of this writing, we don't have a response from the NAPARS Board as it relates to members who would get at least the first issue of Volume 11 in 2016 although that offer remains open at this time.

What that means is, for many NAPARS members, this will be your last issue as a part of your NAPARS Membership.

With all that behind us now, let's address an overview of the changes scheduled to take effect beginning January 1, 2016. More details will be posted to the Collision Magazine website including: 1. New agreements for industry partners will be offered allowing a more even playing field for all organizations 2. New lower price for subscription renewals to reward our loyal subscribers 3. A steeply discounted subscription rate to those who attend either the CDR User's Summit or ARC-CSI Crash Conference 4. A one-time, discounted subscription renewal rate for all NAPARS members who don't want to risk missing an upcoming issue Visit www.collisionmagazine.com/special Offering lower subscription rates and special discounts was something we were prohibited from doing under the previous contract but now, in large part because of a more efficient process overall without lowering quality, that's something we will be able to do!

On to new developments - registration is now open for both the 12th Annual CDR Summit (January 2016) and the 15th Anniversary ARC-CSI Crash Conference (May 2016).

sessions planned for case studies and clarification of concepts and Data Limitations as well as a primer on sources of related information or documentation.

Moving forward to May 2016 we will host our 15th Anniversary of the ARC-CSI Crash Conference in Nevada. This year, as in the past, we will conduct multiple, fully instrumented crash tests (that have been the basis for valuable insight into how various crash scenarios occur and have become the basis for a wealth of information on vehicle systems and operations) in the NASCAR in-field area of the Las Vegas Motor Speedway on the first Monday of the conference. Attendees will gather in a private airconditioned meeting room with a panoramic view of the NASCAR track and have an opportunity to participate in a variety of activities and presentations during the "down-time" between crash tests.

Make no mistake, we will have some special surprises for our 15th Anniversary! Following crash day we are back at the conference hotel for three days of technical presentations from leading subject matter experts and a variety of vendors in the exhibit hall. The ARC-CSI Crash Conference prides itself on bringing a variety of exceptional speakers and presentation topics to expand your knowledge of collision reconstruction and investigation.

Speaking of the ARC-CSI Crash Conference, included with this issue of Collision Magazine is a custom flash drive in the shape of a business card that contains over 7 GB of the data collected at the 2015 ARC-CSI Crash Conference. We have outgrown the data capacity of a DVD (normally included with magazine) and have upgraded to a smaller, faster flash drive to deliver all of the crash data, videos, photos, and PDF presentations. Enjoy!

Sincerely,

Managing Editor

In addition to the latest from Bosch, OE's and others specific to the current state and future of the Bosch CDR Tool, the primary topics for the 2016 CDR User's Summit will be related to information security, data privacy and data integrity. Topical and timely CDR Tool applications topics won't be left out with

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Collision Magazine - Volume 10 Issue 2 7

I

n October, 2015, the leadership of New the England Association of Insurance Fraud Investigators (NEAIFI) spearheaded a truly landmark Joint Conference in Mendon, Massachusetts bringing together insurance companies and law enforcement agencies from around New England. The two day conference featured more than 12 fully documented and instrumented crash tests from simple guard rail, pole or Jersey berm impacts to high closing speed, multiple car tests, snow plow sideswipes of parked cars and much more. Dubbed "Crash & Learn" this 2015 conference was successor to the NEAIFI "Crash and Burn" conference in 2011 and was, again, a success because of the efforts of a number of people and organizations involved in the planning for months.

A truly meaningful conference can't be thrown together 45 days out and the NEAIFI planning group clearly understood that having met for months ahead of time securing the training venues, cars for testing, recruiting and involving public and private sector groups, companies and agencies alike resulting in a memorable and valuable training opportunity. The NEAIFI conference planning committee was headed by Mike Merolli, MAPFRE Insurance & NEAIFI President; Kerri Sugrue, Geico Insurance & NEAIFI Vice President, Suzette Cavanaugh, Pilgrim Insurance & NEAIFI Secretary; Tim Sweeney, Allstate & NEAIFI Treasurer NAIFI Board of Directors: Rick McMaster of Liberty Mutual Insurance, Derek Byron of Plymouth Rock Assurance, Mike Petrillo of Norfolk & Dedham Insurance, Jonathan Almos of Electric Insurance, Sergeant Tim Dowd of The Massachusetts State Police, Chief Ernest Horn of Mendon, MA Police; and NEAIFI Council, Attorney David O. Brink. Even with a group that large and diverse, things got done.

That could even be the theme of "Crash and Learn:" things got done. The two day conference started with more than 70 people from both the public and private sector attending an 8 hour program offered by the Collision Safety Insti-

8 Collision Magazine - Volume 10 Issue 2

tute on incorporating EDR/CDR data into a momentum based crash analysis. Using crash test data from conferences such as the annual ARC-CSI Crash Conference and others, those in attendance were exposed to a review of vector analysis and how graphically how that relates to some of the data elements in crash data retrievable using the Bosch CDR Tool.

The second day was dedicated primarily to crash tests and track side demonstrations. Starting and ending the day on schedule and on time, the more than 255 who attended the field day saw more than a dozen crash tests ranging in severity to someone backing into a pole to a three car in-line crash with the "striking vehicle" coming in at over 30mph.

While much of the focus of the testing was on damage analysis in general, the design of the tests gave those in law enforcement and those in the insurance industry or private practice something they could come away with and apply back at their regular jobs.

For example, one series of tests were conducted with guardrails, Jersey berms and poles installed specifically by Liddell Brothers, Inc., and National Grid for this event. In one series, the test vehicle was driven into the Jersey berm (and another into the guard rail) at various speeds and angles.

Acceleration and EDR data was recovered and damage photographed. Down the road, those in attendance will have seen the damage first hand and whether it's a "hit while parked/unoccupied" in the "insurance world" or a hit and run in law enforcement, seeing the dame and the dynamics, seeing the tire marks on the berm or the paint transfer to the car is an invaluable bit of experience.

With NEAIFI and the allied organizations combining their efforts, focusing on what's important to the members, member organizations and agencies and developing a solid plan in advance and then implementing that plan, we can say that, for "Crash and Learn" 2015, THINGS GOT DONE.

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A conference of this magnitude couldn't have just come together on its own, a number of entities big and small contributed time, manpower and physical resources to make "Crash and Learn" happen. Among those organizations directly participating in the conference success are: • • • • • • • • • • • • • • • •

MAPFRE Insurance Geico Insurance Plymouth Rock Assurance Lieutenant Andrew Klane, Sergeant Timothy Dowd, and The Massachusetts State Police Collision Analysis Section Detective Sergeant Marcus Eddings and the Boston, MA Police Department Officer Roger Wiseman and the Worcester, MA Police Department Mendon, MA Public Safety Director Ernest Horn Sgt. Matt Hoar and the Central Massachusetts Law Enforcement Council Crash Reconstruction Team Sergeant Jeff L'Heureux and the Rhode Island State Police Deputy Chief Mark Bucchino and The Mendon, MA Fire Department Craig Bernard and Liddell Brothers, Inc. Paul Larson and National Grid Alan Tetreault and the Mendon, MA Highway Department Alan Davey and Karcraft of Uxbridge, MA The Collision Safety Institute Collision Publishing

Editor's Note: For better or worse, fall seems to have traditionally been the time of year for a number of the professional association annual conferences and while especially this year it seems like there was "organizational inattention" relative to some of the annual meetings and conferences compared to years past, other groups have stepped up to provide relevant and timely regional training opportunities.

In this issue of Collision, we'll highlight two such training opportunities, one offered in Massachusetts in fall 2015 and the other in Illinois in fall 2014 and the Case Problem in this issue uses data directly from one of the crash tests from the NEAIFI Crash and Learn Conference. www.collisionpublishing.com

Collision Magazine - Volume 10 Issue 2 9

A Team Approach to Crash Investigation Lake County, Illinois Adam Hyde and Joseph Manges Major Crash Assistance Team (MCAT) Deputy Commanders and Crash Test Team

Michael DiTallo Crash Consultant and Crash Test Team

T

he "Team Concept" Overview In 2003, the Lake County (Illinois) Chiefs of Police Association identified the need for a team of specially trained police officers with expertise in crash investigation to be organized and available to assist allied regional police agencies with serious traffic crashes.

The result was a formalized team of well trained crash investigators which became known as the Major Crash Assistance Team of Lake County, or MCAT. The team is governed by a board of directors and operationally consists of a commander, four deputy commanders and sixty crash investigators that cover thirty six police jurisdictions covering mainly Lake County, Illinois – mainly the area north of Chicago, east to Lake Michigan and north to the Wisconsin border - as well as some jurisdictions in three adjacent Illinois counties.

The team's function is quite simple, but its success hinges on close cooperation and a solid working relationship between the member police departments, State's Attorney's office and coroner's office. Since 2004, MCAT has responded to well over 250 fatal and serious injury investigations. A successful prosecution rate demonstrates the sheer cooperation between all entities of the justice system.

Made up of members of the various allied agencies, an MCAT team is available to respond to requests from one of the regional agencies looking for assistance handling serious injury or fatal crashes where MCAT member expertise can be effectively employed. Each scene is handled by a commander and six to eight crash investigators. At each incident, the team completes all at-scene investigative duties (i.e.: forensic measurements, CDR Tool downloads, photographs, scene video, diagrams, vehicle examinations, electronic device examinations, and more) and all investigative findings are returned to the agency responsible for the original investigation upon completion of the investigation team's workup. Special request reconstructions are also completed by team skilled reconstructionists from the team.

10 Collision Magazine - Volume 10 Issue 2

Success of the Regional Team Approach MCAT was formed with the idea that agencies within a defined geographic region could pool both human and physical resources in a larger effort to improve public safety generally and, more specifically, traffic safety. When the creation of MCAT was first proposed for Lake County, there wasn't much information available from the larger law enforcement crash investigation community related to a multi-agency team such as was being proposed. Much of what was learned by the leadership of MCAT was a function of previous individual experience, trial-and-error, and an unprecedented collaborative effort cutting across a variety of agency lines. There were examples of Multidisciplinary Accident Investigation Teams (MAIT) being formed from coast-to-coast from the California Highway Patrol MAIT to the South Carolina Highway Patrol MAIT but those, as with so many others, were teams built within a single agency. Surely, some had members from various sections or specialties within that agency but they were still one agency units. MCAT was proposed as a multi-agency entity which, from the start, presented a new and different set of obstacles.

Where a single agency team has one internal set of policies and procedures to incorporate into the operational structure of the unit, from the start, MCAT was designed as a multi-agency organization and, from that outset, there had to be at least an awareness of the policy variations between agencies such that members of a variety of agencies could work together within MCAT. MCAT was designed as a "call out" type of unit, where teams from MCAT each composed of members from a multiple agencies, would respond when team activation occurs. The initial proposal was such that all "on duty" investigators would respond to the crash scenes, but the team management quickly learned that additional assistance was needed by off-duty personnel. Currently, team responses are generally covered with a 60/40 on-duty, off-duty response split.

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Members of Peter Ahern's MCAT Investigative team hard at work on the aftermath of "their" crash test

Investigators forensically measuring and documenting a motorcycle crash scene

The first crash test of the training day, a Chevrolet rearending a stopped motorcycle with rider

An angled crash involving a station wagon and a Chevrolet

The final of four tests, a Pontiac rear-ending a car and pickup stopped at an intersection www.collisionpublishing.com

Collision Magazine - Volume 10 Issue 2 11

It took no small amount of work to coordinate the potential call out and overtime differences between the involved agencies and, from time-to-time, there are potential struggles in this regard, but overall, with good communication and coordination between the members of the various agencies represented in MCAT and the Chiefs of Police Association, this hurdle has been largely overcome.

Where various resources were to be effectively shared between a variety of agencies, another area of potential conflict was what one agency's "fair share" should be to MCAT compared to that of another agency. Where there would be different size departments involved in the single larger project, an area of concern could be that one agency might feel like it was contributing "more" than another whether it was in terms of personnel or tangible resources or even the potential for overtime. To this day, this still becomes an issue from time to time, but the member agencies realize the true benefit of a team response when the "big one" happens and resources from all across the area respond and devote manpower to help their agency.

For someone contemplating building a new entity along the lines of MCAT, it must be clear that accommodations would have to be made to make sure things are, for lack of a better phrase, "even" between the participating organizations. MCAT currently covers 36 policing jurisdictions, some ranging from as few as four full time police officers, to others that have over 100 officers. The team has approximately 60 members, with most agencies contributing one to two investigators and those investigators still performing normal assignments at their home departments in addition to their MCAT collateral duty. The leadership of MCAT works to make sure that the potential overtime or minimum staffing impact on one agency isn't greater than that of another. As the number of members from participating agencies has increased, staffing teams to minimize conflicts has admittedly become easier, but as things like local special events or manpower restrictions draw on an agency's resources, the effect is felt within MCAT. In 2014 and the first nine months of 2015, MCAT responded to 57 calls from allied agencies for technical onscene or specialized assistance. While no one agency in the region really stands out as having made more requests for assistance than another, anyone in law enforcement knows that there are going to be areas which draw more attention than others and this is especially true in terms of crash investigation. If for no other reason than roadway volume, there are areas where there is simply going to be a greater potential for crashes on a given roadway.

With that in mind, one way of "evening out the load" between agencies and addressing "problem" traffic areas is 12

Collision Magazine - Volume 10 Issue 2

to develop plans to work with other entities such as the Illinois Department of Transportation (IDOT) to identify and evaluate areas with higher than otherwise normal crash rates and, given the expertise available within MCAT, work to develop plans to try to reduce the number of crashes going forward. For instance, currently after all fatal crashes in the MCAT area, an IDOT representative is provided with crash details and subsequent investigation documents. The information from MCAT is used to assist with future highway safety improvement plans and occupant safety efforts.

The MCAT model has been utilized in other areas across the country as a template for inter-governmental coordination at low cost. Original funding for MCAT was through donations by various member agencies. Yearly contributions from the member departments - ranging from only $250 to $750 - allow the team to maintain training and equipment. Each member agency covers all case investigation expenses and their investigator's salary when they are assigned to a call out.

One of the many areas all of the involved agencies benefit equally from is training. Making training a priority lead MCAT to require and provide for continuing education for all investigators. The team sponsors Northwestern University Center for Public Safety courses annually and requires multiple training days throughout the year.

However, even with all the training opportunities, turnover common within police agencies the world over and the ongoing development and redevelopment of best practices across a variety of emerging technologies, individual member backgrounds and agencies is still a challenge. Examples of training include a yearly "new member" orientation and bi-yearly mandatory CDR Tool Technician level certification.

Training During the early formative period for MCAT, when the organizational roster was smaller, it became clear that the backgrounds, levels and types of training across the members of the group varied significantly. With an understanding that the best end product comes from consistent and meaningful training, the leadership of MCAT took advantage of the opportunity of not only pooling human and equipment resources but training opportunities. Keeping the MCAT members up-to-date on the latest in technology and techniques has become both an opportunity and a priority for MCAT. To help guide, train and mentor all team members, the MCAT leadership examined live crash testing and simulated at-scene work using those crashes as an option. The

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goal was not only to have each investigator witness the crashes, but then respond to the scene of each crash as a simulated call-out and, with the guidance of senior team members and professional crash investigation trainers, "investigate" the crash. After their investigations, teams would present their findings and full reports.

The testing seemed to be an overwhelming feat, so the team "phoned some friends." MCAT's requests to area experts was well received and soon the team had the help of experts from around the country, including: the Collision Safety Institute, Dynamic Safety and Cooper Barrette Consulting. The training evolutions and crash test planning took well over a year, but the end goal remained focused on the crash investigators and how they could perform their jobs better and produce the most thorough product.

Roger Barrette explaining at-scene process and procedure to one of the groups of Illinois State's Attorneys who were on hand for the training day

The crash tests were arranged and seven teams were developed and balanced based on the identified investigative strengths and levels of prior training of the various members. Up and coming leaders were identified and acted as "Team Leaders" for all testing.

Then on September 4th, 2014, 10 years after the formation of MCAT, the more than 125 people in attendance witnessed the series of crash tests, observing first-hand the sounds, smells and sights of the metal ripping and cars sliding. Soon after, the teams went to work with the all-important mission at hand: to produce the best and most thorough crash investigation (and have the opportunity to tell the world about it here in Collision Magazine!).

All of the teams did an amazing job. During all the investigations, Airbag Control Modules were imaged, thousands of photographs were taken, and great scene CAD diagrams were developed. Teams conducted skid testing, scrutinized, debated and documented roadway and vehicle evidence and worked together with a single goal. They produced full investigative reports and gave presentations about their investigations the following day. In the end, Team Leader, and Mundelein Police Officer Peter Ahern and his team took the MCAT gold medal for the most thorough investigation and well prepared report. As a team Leader, Ahern has offered his insight into the training experience featured in a side bar to this article.

Two crash teams evaluating their crash scenes and determining their investigative strategies

Thomas Green meeting with some of the State's Attorneys on hand discussing crash survivability

At the end of the day, the MCAT experience demonstrates how a multi-agency team can be designed, developed and trained to work as a single, cohesive unit and then function effectively across a number of otherwise challenging conditions in the larger traffic safety effort. www.collisionpublishing.com

Side impact crash test with van Collision Magazine - Volume 10 Issue 2 13

An MCAT Team Leader's Perspective By: Peter Ahern, Mundelein Police Department While crash investigation training based on live crash tests was a new idea for me, it turned out to be a truly great experience. Not only was I able to participate in this unique training, but I was given the privilege of being appointed to lead one of the seven teams that would be conducting the investigations of one of the four crashes.

I coordinated my team by contacting and meeting each member, speaking with them and learning more about their individual strengths, areas of personal interest or ability and discussing concerns about their potential assignments before assigning them investigative tasks for the crash day. My team consisted of seven investigators from all different agencies within MCAT and nearby jurisdictions: Jason Axelrod (South Barrington PD), Eric Chrabot (Mundelein PD), Greg Czernecki (Arlington Heights PD), John Parello (Antioch PD), Tom Robertson (Wauconda PD), Josh Singer (McHenry County Sheriff) and Waymon Vela (Round Lake Park PD).

On the second day, we opened with training from Rusty Haight (Collision Safety Institute) and then each group worked to complete their investigative reports, while I completed the presentation. Our team overcame some technical issues, but we came together and presented a very thorough investigation presentation to all participants.

I thought this training was an outstanding opportunity for everyone involved. It was nice having crash test scenes that allowed the element of time to thoroughly think through and complete all tasks. All of the crash investigators from the various departments assigned to my team worked well together and exchanged great investigative practices. Less experienced team members had an unparalleled opportunity to practice their new skill set and everyone was afforded the chance to interact with area experts to learn best investigative practices. Finally, the testing allowed everyone the opportunity to experience something they could not learn from classroom lecture or literature review, and all benefited from a confidence boost for when they respond to the "real thing."

Each investigator was assigned a task they felt comfortable with and that they expressed an interest in doing keeping in mind that challenging an individual in a training scenario is an important part of the growth process. With that in mind, we discussed the great opportunity at the crash testing to practice and learn about other investigative areas they may be uncomfortable with, with the goal of affording them the opportunity to grow as investigators.

The crash testing day was long and intense, but was a great experience! The day began with a presentation about proper on-scene crash investigation techniques by Roger Barrette (Cooper Barrette Consulting) and a synopsis of the crashes planned for the day. We then had the opportunity to watch three of the four crashes, but were not permitted to watch our own crash so we could "respond" as in a real world situation.

My team investigated a crash involving a side impact of a Chevrolet Astro Van and Oldsmobile Alero. After reviewing the scene and all duties, my team successfully completed their scene investigative responsibilities, including: photography, videography, evidence identification and marking, forensic measuring, ACM downloads, vehicle examinations, skid testing, crush damage measurements and field sketches. The crash investigations went well into the evening, but were worth every minute. After all data was gathered, the report generating and presentation work began.

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During the MCAT hands-on training day, planners laid out four crash tests all within sight of each other on four intersecting roadways. Supervising team members and instructors were able to move easily between the crash "scenes" to offer assistance and guidance as the teams "investigated" their crashes

Twenty three industry experts and police investigators came together to offer training to over 125 in attendance for the MCAT training meeting

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VIEW E R al i r o Edit

The Rest of the Story Ben LeMere Berla

T

he accident reconstruction community has a tough job. Recreating a crash with limited to no EDR data is tough. It's a good day when an EDR download provides 20 seconds of pre-crash data. The digital forensics community has the opposite problem.

Months or years worth of data are recovered during an investigation, leaving an overwhelming amount of data to sift through.

had previous arrests for driving under the influence. Police went to his home and the owner's girlfriend answered the door. She had a bandage on her head. She said that she got hurt helping to replace a skylight at her mother's, and that she and her boyfriend were there most of the evening.

Given the scenario, once investigator identify a suspect vehicle, bringing in a digital forensics examiner could be critical to the case. By acquiring data from some of the other modules in the vehicle, more can be learned about what happen before, during and after a crash.

Accident Reconstruction Investigators and Digital Forensics Examiners are similar, but they investigate an incident from opposite ends. Accident Recon looks at what happened in the seconds before and during a crash while a digital forensic examiner is looking at what The infotainment and telematics systems in vehicles can hold a significant amount of data rehappened hours before and after a crash. lated to user activities, navigation information, Scenario: A woman and her 8-year old son were and event data. In the scenario above, being able on their way home from his baseball game. Be- to place the vehicle at the scene of the incident cause it was a beautiful night, they took the sce- is critical, but also being able to show where the nic back roads. As their car was coming around vehicle was prior to the incident could be just as a corner, it was hit by what the son described important and help develop a stronger timeline as a dark SUV without its lights on. Their car or event chain. Consider the following; spun out and the mother was knocked unconscious. The dark SUV reportedly swerved, hit Upon getting permission to access the suspect a small group of trees then sped off. With only SUV, the infotainment and telematics systems the physical crash scene evidence and the state- were downloaded. Investigators are able to dement from an 8-year-old, investigators set off in termine from the tracklog history that the SUV the direction the vehicle came from to question was traveling down the same roads that the acpeople in the nearest town. Based on tips from cident occurred on at approximately the same several locals, investigators learned the name of time. The SUV had come from the Hideway the likely vehicle owner. His record showed he Bar and according to the infotainment system's

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event log, the head lights were not turned on when the SUV left the bar. The event log also shows the vehicle being placed into reverse and drive several times at the scene of the accident off the side of the road, near a section of trees. The tracklog history shows the SUV departing the scene of the accident and heading to the local hospital. Several hours later, the SUV navigates from the hospital to a location saved "home" in infotainment system.

About Berla: Berla is a forward-thinking team of digital forensics and cyber security experts providing tools and training for the law enforcement, accident reconstruction and insurance fraud investigation communities. iVe, Berla's Vehicle System Forensics hardware/software kit, is a non-destructive, forensically sound solution that pulls user data, navigation data and vehicle events from a system and parses that data so the investigator can easily sort and examine it. Examples of data include connected devices, call logs, contacts, text messages, Bluetooth connections, wireless connections, routes, saved favorite destinations, tracklogs, doors opening/closing, gear shifts, lights turning on/off, odometer readings, and more.

The digital forensics element supplements EDR data to show a more comprehensive picture of events before, during and after an accident – creating a stronger timeline and therefore a stronger case. With data from EDR acquisitions as well as acquisitions from the infotainment and telematics systems, the moment an accident occurs becomes a longer narrative with a story that is more clearly un- To learn more, contact Berla: derstood than with EDR data alone.

443-333-9301 | www.berla.co

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ED

EW EVI

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R PEE

Kinematics of braced, un-braced, & outof-position occupants in low-speed bumper-to-bumper rear impacts Lawrence A. Wilson P.E. Wilson Consulting, LLC

Sean Haight collision safety institute

A

bstract Five low-speed rear crash tests involving instrumented human test subjects were conducted at the testing and proving grounds of the Las Vegas Motor Speedway as part of the 2014 ARC-CSI Crash Conference. These crash tests were front-to-rear bumper-to-bumper crash tests involving a Chevrolet Cobalt and a Toyota Prius.

Two of these tests involved analysis of belted occupant kinematics when the test subject was braced and fully expecting the rear impact and when the test subject did not anticipate the rear impact.

The impact speeds for the bullet vehicle were approximately 12 mph and the resulting delta-Vs for the target vehicle were approximately 6-7 mph.

Even though the seatbelts locked up in both tests, there were appreciable differences in occupant kinematics when comparing these two tests. When the occupant anticipated the impact, he was able to maintain his grip on the steering wheel and brace his back against the seat back, thereby limiting the amount of lower extremity, upper extremity, and torso movement. However, neck flexion was increased.

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Two low-speed rear crash tests were conducted to analyze the occupant kinematics related to seat back rebound. In one of the tests, the occupant was belted in a normal seated position. In the other tests the occupant was in a normal seated position, but unbelted. The impact speeds for the bullet vehicle were 10-11 mph and the resulting delta-Vs for the target vehicle were 6 to 7 mph.

For the belted occupant, the seatbelt locked after the occupant rebounded off the seat back resulting in a maximum forward head excursion of approximately 10 inches. The corresponding maximum resultant head velocity relative to the vehicle was approximately 11 ft/s. For the unbelted occupant, the maximum forward head excursion was approximately 16 inches and the maximum resultant head velocity relative to the vehicle was approximately 10 ft/s.

An additional low-speed rear crash test was conducted to analyze the occupant kinematics of an out-of-position occupant who was belted and leaning forward. The impact speed of the bullet vehicle was approximately 6 mph and the resulting delta-V of the target vehicle was approximately 4.75 mph.

Following the occupant's initial rearward movement into the seatback, he rebounded forward, but not far enough to return to his original location.

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The testing described in this paper was conducted at and was part of the ARCCSI Crash Conference which is held annually in Clark County, Nevada at the Las Vegas Motor Speedway. During the Crash Conference, one day is dedicated to a series of fully instrumented and documented crash tests. Each year for the last 14 years, the ARC-CSI Crash Conference has featured 8 to as many as 14 crash tests conducted live where those attending the conference can not only watch the tests live but, at the end of the conference, will leave with - as found with this issue of Collision Magazine - a flash drive full of photos, videos and data collected during the testing series. Human subjects involved in the testing, as described in this article, are often monitored by 15 channels of data or more including accelerometers placed on their bodies and load cells on their seat belts. www.collisionpublishing.com

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As a result, minimal shoulder belt forces were measured and the seatbelt had minimal, if any, effect on the occupant's kinematics. The impact caused the occupant's body to slide rearward toward the seat back and rotate toward the seat back. The occupant maintained his leaning forward posture through the seat back rebound phase for approximately 500 msec after the impact.

I

ntroduction/Background

There is no formal definition of a low-speed impact.

However, a general consensus can be readily developed by reviewing relevant published literature. In this section, characteristics of low speed impacts will be presented as defined by vehicle damage, vehicle dynamics, and occupant kinematics.

Vehicle Impact Severity The Insurance Institute of Highway Safety [IIHS] conducts tests on front and rear bumpers. The target impact speeds for these tests are 6.3 mph [10 kph] for full overlap frontal and full overlap rear collisions and 3.1 mph [5 kph] for small overlap "corner" tests [1]. These tests have been referred by IIHS as "Low-Speed" crash tests [2]. The purpose of these tests is "to promote compatible, stable, and energy-absorbing interfaces among vehicles in the fleet."

[1] In the full overlap tests conducted at 6.3 mph, vehicles typically rebound off the fixed barrier, resulting in delta-Vs that are greater than the 6.3 mph impact speed. There is typically vehicular damage resulting from these tests that is measured by IIHS in terms of repair costs. For mid-size cars, the worst performer was the 2006-2010 Pontiac G6 with an average repair cost for the four tests of $2,932. In the full frontal barrier test conducted at 6 mph, the Pontiac G6 exhibited bumper, grill and hood damage resulting in repair damage totaling $5,301. By contrast, the 20012013 Subaru Legacy was the best performer, resulting in an average repair cost for the four tests of $855.

IIHS also conducts a dynamic "whiplash" test as part of their testing and rating protocol for head restraints [3].

This test utilizes a 50th percentile male BioRID dummy that is seated in an OEM seat, which is then accelerated on a sled to 10 mph in 91 msec. with a peak acceleration of 10 g. [3] The BioRID dummy was developed from the hybrid-III crash test dummy, but is equipped with a biofidelic neck capable of measuring rear impact neck loads and the risk of whiplash injuries. The crash test dummy is belted in order to restrain the dummy's forward motion caused by the seat back rebound. However, the seat belt is configured and mounted to the sled and is not representative of the OEM restraint system. The European New Car Assessment Programme [Euro NCAP] also conducts a sled-based whiplash test. In the EuroNCAP whiplash 20

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test, the sled is accelerated to 10 mph for the "low severity" test and 15 mph for the "high severity" test [4]. The focus of both of these tests is risk of injury associated with the initial rearward motion of the occupant into the seatback and head restraint. These tests measure the dummy's time to head restraint contact and forward acceleration of the dummy's torso. The dummy's injury response criteria includes upper neck shear force and upper neck tension force.

McConnell, et al. defined "low velocity collisions" as "motor vehicle collisions in which the impact related change of the rearended vehicle's velocity" was 8 mph or less. [5] Similarly, Szabo, et al. defined "low speed rear-end impacts" as vehicle collisions "in which the struck vehicle sustains a change in velocity (Delta V) of 13 kph or less." [6] Bailey, et al. expanded the definition of low speed impacts.

These researchers conducted vehicle-to-vehicle crash tests with human test subjects, which were defined as "minor impacts." [7] The authors defined a minor impact as an impact in which "tire forces and/or restitution effects cannot be ignored." The authors specifically noted that the term "minor impact" did not reference vehicle damage or occupant injuries. [7] These tests involved delta-Vs up to 12.5 mph [20 kph]. The "onset of symptoms" for the test surrogates in rear impacts corresponded to vehicle deltaVs at or below 5 mph. For frontal impacts, the "onset of symptoms" for the test surrogates corresponded to vehicle delta-Vs of 7.5 to 12.5 mph. The test surrogates were not instrumented and any symptoms were self-reported and not diagnosed by medical professionals.

Cipriani, et al. expanded on the concept of restitution forces playing a significant role in the delta-Vs of the bullet and target vehicles. These authors conducted frontto-rear low speed collisions which yielded coefficients of restitution as high as 0.6. [8] Coefficients of restitution are typically lower for high speed collisions in which there is significant residual crush. Vehicles subjected to 35 mph vehicle-to-barrier crash tests conducted by NHTSA under their New Car Assessment Program [NCAP] typically exhibit lower coefficients of restitution.

Low-speed impacts typically involve impacts with delta-Vs of no more than 15 mph. When subjected to delta-Vs of less than 10 mph, vehicles may not sustain visible damage. In bumper-to-bumper low speed crashes, the vehicles typically do not sustain damage to suspension components and structural components. The damage is limited to bumper assemblies, grills, headlamp assemblies taillight assemblies and body panels. Real world crashes involving low-speed vehicle-to-vehicle impacts typically result in no

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impact gouge marks left on roadway from the under carriage of vehicles.

Occupant Kinematics A consortium of researchers at Chalmers University, Autoliv and Saab conducted research on the effects of seat belts and crash pulse on occupant kinematics [9]. Part of this research included five sled tests using a 50th percentile male hybrid-III dummy equipped with a biofidelic RID neck. Delta-Vs of 3.1 mph [5 kph], 7.8 mph [12.5 kph], and 15.6 mph [25 kph] were applied to the sled in which the dummy was belted. The dummy was also tested in an unbelted configuration at a delta-V of 7.8 mph. These researchers found that "the retractor was only capable of reducing a fraction of the large seat belt slack that was introduced during the rearward motion of the body relative to the seat during the initial stage of the crash event."

Seatbelt loads were not detected until "late in the crash event, when the dummy torso was moving forward relative to the sled as a result of the seat-back rebound." In his PhD thesis, Nilson expanded on this research for 7.8 mph delta-V tests, which were characterized as "low severity impacts."[10] The maximum forward velocity of the chest relative to the seat was 6.7 to 9.6 mph. Nilson, et al did not differentiate which results corresponded to the belted and unbelted tests.

Nilson also conducted tests on out-of position occupants in which the dummy was leaning forward at the time of impact. In these tests, the dummy exhibited no forward motion as a result of seatback rebound. However, these tests were conducted using crash test dummies, which do not model active muscle tension in humans. [10] A defining characteristic of low-speed impacts is that active muscle tension can affect the kinematics of the occupant. Ivory, et al. conducted research on six human test

Test

Bullet Vehicle

1 2 3 4 5

Target Vehicle

subjects who were subjected to rear impact crash tests with an impact speed of approximately 7.5 mph, resulting in delta-V's with the approximate range of 5 to 6 mph. [11] Each test subject applied brakes to the stationary target vehicle as if they were stopped at a traffic light. In about half the tests, there were no audible or visual cues to alert the occupants of the impending impact and therefore, did not brace themselves for the impact. Each test subject was subjected to a second test of the same impact severity in which they were braced and aware of the impending impact. The test subjects were instrumented and it was found that, in five of the six test subjects, head accelerations were lower in the braced conditions when compared to the non-braced test. Also, these researchers concluded that less overall motion occurred in the tests in which the occupants were braced for the impending rear impact.

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ow-Speed Bumper-to-Bumper Rear Impact Crash Tests – Test Matrix and Set Up

Five crash tests were conducted that involved the front of the bullet vehicle striking the rear of the target vehicle. These tests are described in Table 1. In all of the tests, the bullet vehicle was striking a stationary target vehicle. All tests were conducted during the 2014 ARCCSI Crash Conference at the Las Vegas Motor Speedway.

All tests were captured on standard video and high-speed video. The high-speed video camera [Phantom Miro] captured the occupant impact kinematics at 500 frames per second [fps]. The video camera was mounted to the vehicle and all video analysis was performed relative to the vehicle reference frame with no filtering. The impact speed was measured using a VBOX GPS data logger which captured over-the-ground speed at 100 Hz. Acceleration was measured using Diversified Technical Systems [DTS] Tiny Data Acquisition System [TDAS]. The vehicle triaxial accelerometer was mounted near the vehicle's CG and the occupants were equipped with head accelerometers. The

2006 Chevrolet Cobalt 2006 Toyota Prius 2006 Chevrolet Cobalt 2006 Toyota Prius

Bullet Vehicle Impact Speed 11.9 mph 12.1 mph

Target Vehicle Delta-V after 100 ms 6.2 mph 6.8 mph

2006 Toyota Prius 2006 Toyota Prius 2006 Toyota Prius

11.0 mph 9.5 mph 6.6 mph

6.5 mph 6.1 mph 4.4 mph

2006 Chevrolet Cobalt 2006 Chevrolet Cobalt 2006 Chevrolet Cobalt

Target Vehicle Occupant Status Belted Belted - braced for impact) Belted Not belted Belted - leaning forward out of position

Table 1: Summary of Vehicle-to-Vehicle Rear Impact Crash Tests www.collisionpublishing.com

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acceleration data was captured at 10kHz. The acceleration data was filtered using the SAE J-211 standard. Postimpact velocities of the target vehicle were determined by applying a SAE J211 Class 180 filter and integrating the acceleration data. Load cells were fitted on the lap and shoulder portion of the seat belt webbing.

Tests 1 and 2 - Effect of Pre-Impact Bracing on Occupant Kinematics Tests 1 and 2 involved analysis of occupant kinematics when the occupant was braced and fully expecting the rear impact and when the occupant did not anticipate the rear impact. The bullet vehicle was the Chevrolet Cobalt and the target vehicle was the Toyota Prius. The test subject for Test 1 and Test 2 was a 6' 4-1/2" [>95th percentile] 23 y/o male whose weight was 220 lb [90th percentile]. The occupant was belted in the driver seat and the driver was instructed to adjust the seat and seat back to an appropriate and comfortable driving position (Figure 1). The test subject was instrumented to capture head accelerations and a high-definition video camera was installed to capture the post-impact motion of the occupant.

In Test 1, the occupant understood that was he was going to be subjected to a rear impact. However, he was not provided any audible or visual cues to anticipate the moment of the impending rear impact. For this reason, the occupant did not brace himself with active muscle tension for the impact. In Test 2, the occupant was allowed to use the vehicle's rear view mirror as a visual cue to anticipate the impact. In this configuration, he firmly gripped the steering wheel and extended his arms so that his torso was braced against the seat back as shown in Figure 1.

Tests 3 and 4 – Occupant Kinematics from Seatback Rebound in Low-Speed Rear Collisions Tests 3 and 4 involved analysis of occupant kinematics from seatback rebound in low-speed rear impacts. The bullet vehicle was the Toyota Prius and the target vehicle was the Chevrolet Cobalt. The test subject for Test 3 and Test 4 was a 5' 6-1/2" [10th percentile] 19 y/o male whose weight was 120 lb [<5th percentile]. The occupant was seated in a normal position in the right front passenger seat. The seat was adjusted in the rearmost seating position to reduce the likelihood of occupant contact with the dash panel. The test subject was instrumented to capture head accelerations and a high-speed camera was installed to capture the occupant motion at a rate of 500 fps.

In Test 3, the occupant was belted and seat in a normal upright position. In Test 4, the occupant was seated in a normal upright position, but not belted (Figure 3). In Test 3 and 4, the occupant was not provided any visual or audible cues and therefore, was not braced for the impact.

Figures 2 and 3 show the seating configuration for the occupant for Test 3 and Test 4, respectively. In Figure 2, the occupant raised his left arm so that the seatbelt configuration could be fully photographed. At the time of the impact his arms were resting in his lap.

Figure 2: Occupant Seating Configuration for Test 3

Figure 1: Test Subject Bracing Himself for Impending Rear Impact [Test 2] Figure 3: Occupant Seating Configuration for Test 4 22

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Test 5 – Occupant Kinematics from Out-of-Position Occupant – Leaning Forward In Test 5, the occupant was instructed to lean forward as if reaching for an object on the floor pan. The purpose was to analyze the occupant kinematics of a belted occupant who was actively contracting their torso and abdominal muscles when struck from the rear by another vehicle. The occupant's pre-impact position is shown in Figure 4.

speed rear impacts. For this reason, pre-existing exterior body damage was acceptable as long as the crash pulses and delta-Vs of the target vehicles were consistent between comparable tests. Referring to Table 1, the delta-Vs for Tests 1-4 100 msec after impact varied from 6.1 mph to 6.8 mph, indicating similar impact severities between these four tests. The impact severity for Test 5 was deliberately reduced due to injury concerns for the test surrogate.

Any pre-test seatback damage was deemed unacceptable.

No seatback damage was detected for any of the front seats in the Cobalt or the Prius before or after any of the tests.

Figure 5: Pre-Existing Damage from Target Vehicle for Test 3

Figure 4: Occupant Seating Configuration for Test 5 Given the observed forward movement exhibited by the occupant in Test 3 and 4, there were concerns that the outof-positon test could result in occupant contact with the dash panel, a potential injury mechanism. For this reason, the target impact speed was decreased from 10 mph to approximately 5 mph.

Vehicles – Pre-Existing Damage Figure 5 shows pre-existing damage to the rear of the target vehicle for Test 3. There is a larger scrape to the rear bumper cover right rear quarter panel in the vicinity of the passenger side tail light assembly. There is minor impact damage to the trunk and the trunk is dislodged. This damage was caused prior to acquisition of this vehicle. The cause of this damage is unknown. The primary emphasis for the testing presented in this paper was to document and analyze the occupant kinematics associated with low-

V

ehicle-to-Vehicle Low-Speed Rear Impact Crash Tests – Results and Analysis The results of these low-speed rear crash tests are presented in this section.

Tests 1 and 2 - Effect of Pre-Impact Bracing on Occupant Kinematics Tests 1 and 2 involved the same bullet vehicle [Cobalt] and the same target vehicle [Prius] with the same test subject in the driver's seat of the target vehicle. In Test 1, the test subject was not braced for impact whereas, in Test 2, he braced for the impending impact. A comparison of the test subject's occupant kinematics is presented in this section. Figure 6 shows the delta-V plot of the target vehicle [Toyota Prius] in Test 1, in which the test subject was not braced for the rear impact. The peak delta-V was approximately 6.4 mph. The crash pulse was approximately 90 msec at which point the delta-V was approximately 6.2 mph.

Figure 7 shows the delta-V plot of the target vehicle [Toyota Prius] in Test 2, in which the test subject was able to anticipate the impact and braced himself. The peak delta-

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torso movement, which resulted in a second peak longitudinal head acceleration approximately 600 msec after impact.

Figure 6: Test 1 Target Vehicle Delta-V (from accelerometer)

Figure 7: Test 2 Target Vehicle Delta-V V was approximately 6.8 mph which corresponded with the end of the crash pulse at approximately 100 msec.

There were appreciable differences in occupant kinematics when comparing these two tests. In Test 1, in which the occupant was not braced for the rear impact, the initial rearward movement relative to the interior of the vehicle caused the occupant to lose his grip on the steering wheel as his head and torso moved rearward toward the seatback and head restraint.

As the occupant's head and torso rebounded forward from the seatback, the occupant's maximum forward excursion was limited by the seatbelt retractor locking. The occupant's maximum forward excursion was characterized by his hands extending beyond the steering wheel, nearly coming into contact with the instrument panel. As the occupant's torso returned to the seatback, the occupant's right hand clutched the steering wheel. It wasn't until after the occupant's torso rebounded off the seatback a second time that the occupant's left hand clutched the steering wheel. The occupant's left hand clutched the steering wheel approximately 510 msec after the start of the impact. This action allowed the occupant to mitigate his 24

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In Test 2, when the occupant was able to anticipate the impact, the test subject was able to maintain his grip on the steering wheel and brace his back against the seat back, thereby limiting the amount of movement in the lower extremities, upper extremities, and torso. The occupant's bracing resulted in increased relative motion between the head and torso, which corresponded to an appreciable amount of neck flexion in which the occupant's chin contacted his chest. Ivory, et al. conducted a series of crash tests comparing occupant kinematics for braced and unbraced occupants. Ivory, et al concluded "that less overall occupant motion occurred for the aware and braced condition." [11] Ivory's findings are consistent with the results from the testing presented in this paper, in which the occupant's lower extremities, upper extremities and torso exhibited significantly less motion in the braced condition. Furthermore, Ivory, et al. found that peak head accelerations occurred due to head-to-head restraint interaction, which is consistent with the findings presented in this paper.

However, Ivory, et al. reported that five out of six of their test subjects "head accelerations were lower" in the "aware condition." [11] Even though the peak head velocities were similar, the corresponding peak acceleration was higher in the braced condition [Test 2]. Given that the head velocities were similar, the disparity in peak head accelerations may have been caused by interaction with a a stiffer, less energy absorbent part of the head restraint. Figures 8 and 9 show the longitudinal head accelerations and velocities for Test 1 and Test 2, respectively.

The peak longitudinal head velocity was approximately 8.5 ft/s [5.8 mph] for both tests. The similar peak head velocity between the two tests was most likely due to the similar impact severity [delta-V of 6.4 mph for Test 1 and 6.8 mph for Test 2],

which resulted in similar torso-to-seatback and head-tohead restraint engagement and rebound.

The peak head accelerations, however, were appreciably different between Test 1 and Test 2. When the occupant did not brace himself in anticipation of the impact [Test 1], Figure 8 shows the maximum and minimum longitudinal head accelerations were approximately 5.5 g and -2.3 g, respectively. In Test 2, the occupant had braced his torso against the seatback by gripping the steering wheel and straightening his arms. These actions resulted in minimizing torso movement during the impact, which corresponded to increased relative motion between the head and torso. The increased relative movement between the occupant's head and torso in Test 2 resulted in higher head accelerations for the occupant when he braced him-

self in anticipation of the impact. Referring to Figure 9, the maximum head acceleration was approximately 8 g when the occupant braced himself. The minimum head acceleration was approximately -3.5 g, which occurred at approximately 230 msec and corresponded to the occupant's peak neck flexion and chin-to-chest contact.

In both tests, the occupant interacted with the seatback due to the initial forward acceleration of the vehicle. However, in Test 1 in which the occupant had not anticipated nor braced himself for the impact, he struck the seatback a second time at approximately time = 0.6 seconds.

For both, Test 1 and Test 2, the seatback rebound was insufficient to cause the belted occupant's head to strike the steering wheel, windshield, or dash panel. Likewise, the

Figure 8: Test 1 Occupant Longitudinal Head Acceleration and Velocity Belted, Not Braced (from accelerometer)

Figure 9: Test 2 Occupant Longitudinal Head Acceleration and Velocity Belted, Braced for Impact (from accelerometer) www.collisionpublishing.com

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Test 3 and Test 4 – Occupant Kinematics of Seatback Rebound

Figure 10: Test 3 Target Vehicle Delta-V (from accelerometer)

Figure 11: Test 4 Target Vehicle Delta-V (from accelerometer) seatback rebound was insufficient to cause the occupant's legs to strike the knee bolster. There was no complaint of pain or injury immediately following these tests. No subsequent injury symptoms were reported by the test subject the following day.

Test 3 and Test 4 – Vehicle Dynamics Figure 10 shows the delta-V plot of the target vehicle [Chevrolet Cobalt] in Test 3, in which the test subject was belted and sitting in a normal upright position. The peak delta-V was approximately 7.1 mph. The crash pulse was approximately 125 msec at which point the delta-V was approximately 6.8 mph.

Figure 11 shows the delta-V plot of the target vehicle [Chevrolet Cobalt] in Test 4, in which the test subject was not belted and sitting in a normal upright position. The peak delta-V was approximately 6.5 mph. The crash pulse was approximately 100 msec at which point the delta-V was approximately 6.1 mph.

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The head movement and head velocities for these tests were determined by implementing target tracking software to the high-speed 500 fps video footage. For the belted occupant seated in a normal upright position in Test 3, the maximum forward head excursion resulting from the seatback rebound was approximately 10 inches [Figure 12] and the maximum longitudinal head velocity relative to the vehicle was approximately 11 ft/s [Figure 13].

Figure 14-a through 14-c show the occupant's motion at key points during the impact sequence. In Figure 14-a, the occupant is seated in an upright position at the time of impact [Time = 0.0 sec]. In Figure 14-b, the occupant has reached his maximum rearward excursion at approximately 110 msec. In Figure 14-c, the occupant has reached his maximum forward excursion at approximately 290 msec. The seatbelt continued to spool out until the retractor locked at approximately 200 msec.

Figure 15 shows the shoulder belt load as a function of time. At approximately 200 msec, the seatbelt tension starts to increase dramatically, reaching its peak of 120 lbs at approximately 260 msec.

For the unbelted occupant seated in a normal upright position in Test 4, the maximum forward head excursion resulting from the seatback rebound was approximately 16 inches [Figure 16] and the maximum longitudinal head velocity relative to the vehicle was approximately 9.5 ft/s [Figure 17].

Figure 18-a through 18-c show the occupant's motion at key points during the impact sequence. In Figure 18-a, the occupant is seated in an upright position at the time of impact [Time = 0.0 sec]. In Figure 18-b, the occupant has reached his maximum rearward excursion at approximately 104 msec. In Figure 18-c, the occupant has reached his maximum forward excursion at approximately 430 msec.

Figure 12: Test 3 Belted Occupant Head Excursion (from video analysis, + x forward)

Figure 13: Test 3 Belted Occupant Longitudinal Head Velocity (from video analysis)

Figure 14-a: Test 3 Belted Occupant at Time of Impact [Time = -0.2 msec]

Figure 14-b: Test 3 Belted Occupant at Maximum Rearward Excursion [Time = 110 msec] www.collisionpublishing.com

Figure 14-c: Test 3 Belted Occupant at Maximum Forward Excursion [Time = 292 msec] Collision Magazine - Volume 10 Issue 2 27

Figure 15: Shoulder Belt Tension Load for Belted Occupant in Normal Seated Position

Figure 16: Test 4 Unbelted Occupant Head Excursion (from video analysis, + x forward)

Figure 17: Test 4 Unbelted Occupant Longitudinal Head Velocity (from video analysis) 28

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Test 1 and Test 3 – Comparison of Vehicular Damage in Low-Speed Rear Impacts Low-speed testing conducted by IIHS has shown that vehicles can exhibit widely varying degrees of damage when subjected to the same impact conditions. These IIHS tests involve vehicle crash tests into rigid barriers. The purpose of these tests is to measure vehicle damage repair costs and not occupant kinematics. The testing presented in this section shows that vehicles can exhibit a significant disparity in vehicle damage when subjected to bumper-tobumper rear impacts under similar impact speeds.

Figure 18-a: Test 4 Unbelted Occupant at Time of Impact [Time = 0.1 msec]

Figure 19: Pre-Existing Damage from Target Vehicle for Test 3

Figure 18-b: Test 4 Unbelted Occupant at Maximum Rearward Excursion [Time = 104 msec]

Pre-existing damage to the Chevrolet Cobalt is shown in Figure 19. There is a larger scrape to the rear bumper cover right rear quarter panel in the vicinity of the passenger side tail light assembly. There is minor impact damage to the trunk and the trunk is dislodged. This damage is biased toward the passenger side. If the vehicle's rear structural stiffness had been compromised from this damage, the passenger side of the vehicle would have been weaker and less resistant to crush than the driver side. It follows that a centered rear impact would have resulted in asymmetrical crush.

Figure 20 shows the damage to the target vehicle after Test 3 in which its delta-V was 7.1 mph. The damage is centered and symmetrical when comparing the driver side to the passenger side. There is significant residual crash to the rear bumper and the energy-absorbing Styrofoam from the rear bumper has been fully fractured and been dislodged.

Figure 18-c: Test 4 Unbelted Occupant at Maximum Forward Excursion [Time = 436 msec]

Figure 21 shows the damage to the rear of the target vehicle after Test 1 [Toyota Prius] There was no visible dam-

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Figure 20: Damage to Rear Bumper of Target Vehicle after Test 3

Figure 21: Damage to Rear of Target Vehicle after Test 1

Figure 22-a: Front Profile of Toyota Prius

Figure 22-b: Front Profile of Chevrolet Cobalt

age and no residual crush to the Toyota Prius after it had undergone a 6.4 mph delta-V.

The front profiles of the Prius and Cobalt are shown in Figures 22-a and 22-b. Given the similarities in front profiles between these two vehicles, the disparity in damage exhibited by these vehicles when subjected to rear impacts of similar severity is most likely the result of differences of inherent damage threshold tolerance.

When comparing the impact speed of the bullet vehicle and the delta-V of the target vehicle, the impact severity of Test 1 and Test 3, are similar. However, there was an appreciable disparity in the resulting damage to the rear of the target vehicles. The rear bumper of the Cobalt exhibited significant damage to its rear bumper and Prius exhibited negligible damage to its rear bumper. Under similar low-speed impact speeds, different vehicles from the same vehicle class can exhibit no visible damage or can exhibit significant damage to the rear bumper. In bumperto-bumper low speed impacts resulting in delta-Vs under 10 mph, estimating the delta-V based on vehicle damage is not recommended unless the damage threshold for the specific subject vehicle is known.

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Test 5 – Occupant Kinematics of Belted, Leaning Forward Occupant The impact speed of the bullet vehicle for test 5 was 6.6 mph. The resulting delta-V for the target vehicle was 4.75 mph [Figure 23]. The impact speed was decreased due to concerns that the out-of-positon test could result in occupant contact with the dash panel, which was a potential injury mechanism.

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Figure 23: Test 5 Target Vehicle Delta-V (from accelerometer) Figure 24-a through 24-c show the occupant's motion at key points during the impact sequence. In Figure 24-a, the occupant is leaning forward at the time of impact [Time = 0.0 sec]. In Figure 24-b, the occupant has reached his maximum rearward excursion of 9.5 inches at approximately 210 msec. The occupant has maintained a forward leaning position and his torso and legs have rotated rearward, pivoting about his hips and buttocks. This rotation is the primary cause for the vertical excursion of the occupant's head shown in Figure 24-b. In Figure 24-c, the occupant has reached his maximum post-impact forward excursion of 3.4 inches at approximately 550 msec.

During the initial phase of the impact sequence, the occupant's buttocks and hips slid rearward along the seat toward the seat back. The occupant, while maintaining the leaning-forward posture, rotated on his buttocks slightly rearward toward the seat back. The occupant continued to maintain the leaning-forward posture during the entire impact sequence. Consequently, the occupant's contact with the seatback was limited to his lower back.

The maximum forward head velocity during the rebound phase was 2.7 ft/s, Referring to Figure 24-c, the occupant's head ended up approximately 3.5 inches rearward from its initial position at the start of the impact sequence.

Upon impact, the webbing spooled into the retractor while the occupant moved rearward towards the seat back and the webbing spooled out during the rebound phase.

The seatbelt webbing did not develop sufficient tension to appreciably affect the occupant's kinematics. Figure 25 compares the shoulder belt tension of the belted occupant seated in a normal position to the belted occupant

in a leaning-forward position. When belted in a normal seated position, the test subject loaded the shoulder belt to a maximum tension load of 120 lb. When the same test subject was in a leaning forward position, the shoulder belt underwent a maximum tension load of only 5 lb.

"

When belted in a normal seated position, the test subject loaded the shoulder belt to a maximum tension load of 120 lb. When the same test subject was in a leaning forward position, the shoulder belt underwent a maximum tension load of only 5 lb.

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Figure 24-a: Test 5 Belted, Leaning Forward Occupant at Time of Impact [Time = 0.0 msec]

Figure 24-b: Test 5 Belted, Leaning Forward Occupant at Maximum Rearward Excursion [Time = 210 msec]

Figure 24-c: Test 5 Belted Leaning Forward Occupant at Maximum Forward Excursion [Time = 550 msec] 32

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Figure 25: Shoulder Belt Tension Loads for Belted Occupants Normal Seated Position (7 mph Delta-V) v. Leaning Forward (4.75 mph Delta-V)

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onclusions The five vehicle-to-vehicle low-speed rear impact crash tests and the published research presented in the body of this paper form the basis for the following conclusions.

•

•

•

When an occupant [belted or unbelted, braced or relaxed] was seated in a normal upright position and subjected to a vehicular delta-V of 6.4 to 7.1 mph, the occupant's head sustained forward velocities due to seat back rebound of 8.5 ft/s to 11.0 ft/s [5.8-7.5 mph].

cupant's head sustained a maximum forward velocity due to seat back rebound of 2.7 ft/s, which resulted in minimal seatbelt webbing tension. The seatbelt had negligible effects on the occupant's kinematics.

•

- An occupant who anticipated the impending rear impact and braced himself demonstrated the ability to alter his subsequent occupant kinematics caused by the impact. The occupant was able to maintain his grip on the steering wheel and brace his back against the seat back, thereby limiting the amount of lower extremity, upper extremity and torso movement and increasing neck flexion.

When an occupant was belted [braced or relaxed] in a normal upright position and subjected to a vehicular delta-V of 6.4 to 7.1 mph, the seatbelt webbing did not develop sufficient tension to affect the occupant's kinematics until the occupant was moving forward due to seatback rebound. These finding are consistent with those reported by Nilson, et al. [6, 7]

- An occupant who was actively contracting his abdominal muscles [among other core torso muscles] by leaning forward maintained this body position through the duration of the initial rearward excursion and the subsequent forward rebound off the seatback.

When an occupant was seated in a normal upright position and subjected to a vehicular delta-V of 6.5 to 7.0 mph, the amount of forward excursion was reduced from 16 inches when the occupant was not belted to 10 inches when the occupant was belted.

•

When an occupant was belted and seated in a normal upright position, the forward acceleration and velocity caused by the seatback rebound were insufficient to cause the occupant's legs, torso and head to strike the steering wheel, dash panel, or knee bolster.

•

When an occupant was belted, leaning forward and subjected to a vehicular delta-V of 4.75 mph, the oc-

Active muscle tension can strongly influence occupant kinematics on low-speed rear impacts.

- These results are consistent with previous research demonstrating the ability of active muscle tension to alter the occupant kinematics associated with lowspeed collisions.

•

When subjected to a single low-speed rear impact in which the front of the Chevrolet Cobalt struck the rear of the Toyota Prius, the Prius underwent a deltaV of 6.4 mph and exhibited no visible crush as a result the impact.

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•

•

When subjected to a single low-speed rear impact in which the front of the Toyota Prius struck the rear of the Chevrolet Cobalt, the Cobalt underwent a delta-V of 7.1 mph and exhibited measurable crush to its rear bumper.

This disparity in damage threshold between vehicles in the same vehicle class is consistent with, and has been demonstrated by, bumper test ratings conducted

by IIHS.

R

7. Bailey, et al. "Data and Methods for Estimating the Severity of Minor Impacts." SAE Technical Paper Series – 950352.

8. Cipriani, et al. "Low Speed Collinear Imapct Severity: A Comparison between Full Scale Testing and Analytical Prediction Tools with Restitution Analysis." SAE Technical Paper Series 2002-01-0540.

9. Nilson, G. et al. "Rear-End Collisions – The Effect of the Seat-Belt and Crash Pulse on Occupant Motion."

ESV Paper 94-S10-O-07, 1994.

eferences 1. Insurance Institute for Highway Safety. "Bumper Test and Rating Protocol – Version VIII." September, 2010.

10. Nilson, G. et al. "Rear-End Collisions – The Effect of Energy Absorption and Recliner Stiffness on Occupant Motion." PhD Thesis, Chalmers University of Technology – Sweden.

2. Insurance Institute for Highway Safety. "IIHS LowSpeed Crash Test." Slide Presentation, May, 2006.

11. Ivory, M. A. et al. "Brake Pedal Response and Occupant Kinematics During Low-Speed Rear-End Collisions." SAE Paper 2010-01-0067. April 12, 2010.

3. Insurance Institute for Highway Safety. "RCAR-IIWPG Seat/Head restraint Evaluation protocol (Version 3). Research Council for Automotive Repairs – International Insurance Whiplash Prevention Group.

March, 2008.

4. European New Car Assessment Programme (Euro NCAP). "The Dynamic Assessment of Car Seats for Neck Injury Protection testing Protocol – Version 3.2." November 2014.

A

cknowledgements The authors would like to thank ARC-CSI, Biomechanical Research and Testing [BRT], Billy Cox Group, and Biomechanics Analysis for their support in conducting the crash tests that formed the basis of this paper. Also, we would like to acknowledge our tests subjects, Connor Haight and Robert Thompson for volunteering as the test subjects in these tests.

5. MConnell, W. et al. "Analysisi of Human Test Subject kinematic Responses to Low Velocity Rear End Impacts. SAE Technical Paper Series – 930889.

6. Szabo, T. et al. "Human Occupant Kinematic Response to Low Speed Rear-End Impacts." SAE Technical Paper Series – 940532.

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at n tur eel y M wh e h t

DON'T LOOK TOO CLOSELY! Erik Carlsson

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his article is about a legal case that has some similarity with the case described in "A Dirty Trick

By A Law Of Physics, Or What?" from the Spring 2015 issue of Collision. Even in this case, a defendant carmaker retained a testing laboratory that had a reputation for its ability to conduct tests that show the desired results, and also this full scale test ended up showing what the carmaker no doubt did not want the test to show.

The case concerned the severe spinal injuries the driver of a small car sustained when the front section of the roof of her car was pushed down by a wheel that a moment earlier had come off a car traveling in the opposite direction. The accident happened on a straight four-lane two-way road. The driver of the car that lost a wheel stated at the subsequent police investigation that he was traveling at about 40 mph when suddenly the left rear wheel came off. He immediately pulled over onto the shoulder 36

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and stopped, he stated. His passenger told the investigating police officer that she stepped out of the car as soon as the driver stopped, and saw the wheel continuing down the road, bouncing up and down, and crossing over into the oncoming lanes. A driver of an oncoming car in the left lane managed to avoid the wheel, but the driver of a car farther back in the right lane did not. The bouncing wheel came down on the front part of the roof of that car. The roof collapsed onto the head of the driver, compressing her spine.

She survived the accident, but sustained severe injuries. (A passenger in the car brought it to a safe stop after the impact.) The dislodged wheel came to rest on the edge of the road, close to where the eyewitness pointed out where the car was at the time of the impact. www.collisionpublishing.com

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At the law suit that followed, the injured lady's attorney named the carmaker as a codefendant. His allegation was that the windshield header of the car was too weak to offer the front seat occupants a reasonable minimum protection in a type of accidents that are known to happen. In support of his allegation, he relied on strength calculations supplied by a consulting firm that specializes in vehicle safety and accident reconstruction. The consultant firm examined the vehicle after the accident and also obtained an undamaged windshield header for the car model, plus one for a competitive car model for comparison. The car model selected for the comparison was one that had been named by the Insurance Institute for Highway Safety (IIHS) as an example that small cars do not have to be less safe than midsize cars. [The institute had some time before this accident published statistics that showed fatality rate for cars of different makes and models. To make the statistics more comparable, the institute had divided the listed cars in three groups: small, midsize and large. The car named by the institute that distinguished itself from the other cars in the small car group had a fatality rate about equal to the average of the midsize group.]

Another reason for the consulting firm's selection of a particular car model for the comparison of the two windshield headers was that the firm had previously received from the maker of that car a video that showed its different models being tested in accordance with the FMVSS 208 dolly rollover test. The video showed that a test car, occupied by four dummies, rolled over completely three times (thus coming to rest on its wheels.) The roof did not collapse, none of the windows broke during the three roof landings, and the doors could after the test be opened by hand, albeit with some difficulty. [Note: This event took place at a time when the propensity of SUVs to roll over and their roofs to collapse had become frequent TV news, with some interest groups claiming that vehicle roof strength played no safety role in rollover accidents!]

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imilar, yet different. While the two stamped sheet metal windshield headers used for the strength computations did not look very different, the computation of their moments of inertia in vertical direction, or in simpler terms, their resistance to downward bending, showed that the one used for the comparison was three times that of the one in the accident vehicle. Thus, had the injured lady been driving a car of the make and model named by the IIHS as an example of a safer car, or had the car she was driving had a windshield header designed like the one used by the other carmaker, the downward bending of the roof in her car would have been significant less.

She would therefore have escaped the collision with the

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dislodged wheel with far less injuries, if any injuries at all, the plaintiff's attorney alleged.

As could be expected, the defendant carmaker's legal team objected to the allegation that the plaintiff's injuries would have been less if she instead had been driving a car of the make and model used for the comparison of the strength of the windshield headers. The force the dislodged wheel exerted on the roof of the car at the impact was so high that no existing car roof could have protected the driver at this impact, the defendant carmaker's defense team claimed.

The carmaker named as its expert witness one of its engineers, who produced computations of the force the wheel allegedly exerted on the front edge of roof of the accident car. For the purpose of determining the force, the defendant's engineer postulated that the wheel moved horizontally at a speed of 40 mph at the moment of impact, and that it hit the front edge of the roof of the car from straight ahead. Further, that the height of the wheel was such that its center was at the same height above the road as the front edge of the roof. At the impact, the wheel reversed its direction of movement and assumed the speed of the oncoming car, which also was 40 mph. Thus the wheel was according to the defense expert subject to a Delta-V of 80 mph at the impact.

Based on the described scenario, the defense expert simplified the force calculations. The delta-V of 80 mph the wheel was subject to at the collision with the car was comparable to the wheel at 40 mph hitting a solid barrier at right angle, which would cause the wheel to bounce back at 40 mph, for a Δ-V of 80 mph, he stated. The compression of the tire at the impact with the barrier would be five inches, he estimated.

An ingenious simplification of force calculations? The comparison with the wheel striking a solid barrier headon certainly simplified the calculations. The mean rate of deceleration of an object that stops in a known distance from a known speed equals the square of the speed divided by twice the stopping distance. Hence the mean rate of deceleration of the wheel when it hit the imaginary solid barrier was: (40x22/15)2/(2x5/12) = 4,130 ft/s per second, where 22/15 is the exact conversion factor for mph to ft/s. [This is a particularly handy conversion factor whenever the speed to be converted to ft/s is a multiple of 15 mph, such as 30, 45, or 60 mph. We don't need a calculator to tell us that 22 multiplied by 2, 3, or 4 equals 44, 66, or 88. Another good point about the 22/15 conversion factor is that, unlike the commonly used rounded factor 1.467, it prevents the inclusion of erroneous decimals when converting multiples

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of 15 mph to ft/s. In addition, 22/15 requires no more key strokes than 1.467.]

Using the common assumption in accident reconstruction that force is proportional to depth of deformation, the defense expert determined that the peak deceleration of the wheel during the impact with the barrier was twice the mean rate, or some 8,300 ft/s per second [To simplify reading, I have rounded the numbers]. By dividing by 32.2 to get the rate of deceleration expressed in g, he arrived at slightly more than 250 g. The weight of the wheel was some 25 lbs, so the force the wheel exerted on the front edge of the roof of the plaintiff's car was well in excess of 6,000 pounds.

Such a force applied to the front edge of the roof is higher than what any existing car roof can resist, the defendant carmaker's legal team proclaimed.

Well-established solution of vehicle dynamics? Did the carmaker's engineer use a reasonable approach to solve a problem that involved the dynamics of a rolling and bouncing wheel? The defendant's legal team no doubt thought so as the described calculation is part of the legal documents filed in the case. Yet, the assumption that the wheel at a speed of 40 mph hit the front edge of the roof of the oncoming car from straight ahead and then in a near instant simply reversed its direction and moved backwards at a speed equal to its pre-impact speed is unreal. The roof of the car was at the accident pushed down, of which follows that the wheel had to exert a downward force on the roof and therefore had to be above the roof when the driver's spine was compressed. The wheel was crossing the travel lanes at the impact, so the wheel could not have been moving in a direction directly opposite to that of the car. The wheel had to be moving laterally relative to the direction in which the car was moving. The impact would thus cause a force acting on the side of the wheel facing the car. The wheel must therefore have tumbled sideways at the impact and slid off the roof, which explains why the wheel came to rest on the edge of the road somewhere in the area where the impact happened.

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Disregard of the Laws of Physics! Apart from the pure technical questions as to what the movements of the wheel were during and following the impact, there was a fundamental error in the defense argument. The claim that the dislodged wheel exerted a force on the front edge of the roof that was higher than what any car roof could handle is an impossibility, and a direct contradiction of Newton's Third Law that states that a force cannot exist by itself. It can exist only when there is an equal force in the opposite direction. Thus the force the wheel exerted on the roof had to be equal to the force the roof exerted on the wheel. Hence the strength of the

roof was as much a factor in the generated force during the impact as the wheel itself. Yet, the defense expert did not even include the roof, or anything about the car, in his force computations.

"

The plaintiff's attorney naturally dismissed the defense expert's calculations, not only because of the unreal picture the expert painted about the impact, but perhaps even more so because of the expert's disregard of a law of Physics.

"

The plaintiff's attorney naturally dismissed the defense expert's calculations, not only because of the unreal picture the expert painted about the impact, but perhaps even more so because of the expert's disregard of a law of Physics. The defendant carmaker's attorneys responded by advising that they were going to retain a testing laboratory to conduct a full scale crash test, and that they would supply the plaintiff's attorney with the test result when done.

They did!

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he full-scale crash test. To the plaintiff's surprise, the full-scale crash test conducted by the testing laboratory retained by the carmaker included only one car, namely the car model used by the consulting firm for the comparison with the accident car. Perhaps even more surprising was the fact that the test was conducted without dummies in the test car!

The test was set up with the test car accelerated along a test track to a speed of 80 mph, striking a wheel held vertical, and aligned exactly with the test track. The wheel was of the same type as that involved in the accident, suspended at a height such that its center was slightly higher than the front edge of the roof of the test car. [The reason for the selected height of the wheel was no doubt to ensure

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that the wheel would pass over the roof, pushing the roof downwards, rather than having the risk of the wheel entering the interior of the car and pushing the roof upwards.]

It is obvious that the test was set up to simulate the defense expert's scenario with the wheel lined up exactly with the test vehicle, and with a closing speed of 80 mph, rather than to simulate what happened at the accident.

Figure 2 shows the test vehicle and the wheel just before they disappear from the camera view a few milliseconds after the impact.

I include some photos that are generated from the video tape the defendant carmaker supplied to the plaintiff's attorney after the test.

Figure 3: Test vehicle disappearing in a cloud

Figure 1: Test vehicle approaching Figure 1 shows the test vehicle, approaching from the left at a specified speed of 80 mph, at the moment it strikes the suspended, stationary wheel. The thin wires supporting the wheels are not visible in the video.

Figure 3 shows the test vehicle disappearing in a cloud of dust at the perimeter of the testing ground five seconds after striking the wheel. [In the background an airplane is approaching the landing strip at the nearby airport, and can be seen right above the bushes over the disappearing test vehicle.] It is unclear whether the testing laboratory had miscalculated the test vehicle's stopping distance or if the test speed turned out to be much higher than the specified 80 mph, or whether the vehicle's brakes did not perform as expected.

Figure 4: Roof damage of the test car Figure 2: Test vehicle after impact

Figure 4 is a close-up of the roof damage of the test car taken at its point of rest.

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a plastic sheet that was kept in place under the roof panel by what can be described as elastic curtain rods. When the top of the windshield broke, the 80 mph wind entered the space between the roof panel and the headliner, inflating the headliner like a balloon. It can in fact be argued that the separation of the headliner and the roof panel would have had a positive effect if the car had been occupied, and this is that the headliner would have reduced the occupants' risk of being hit by small pieces of glass breaking loose from the laminated windshield.

Figure 5: The wheel at rest Figure 5 shows the wheel at rest after having landed, bounced and rolled in a spiral before coming to rest close to half a minute after the impact. The video supplied by the defendant's legal team did not show the wheel while it was airborne.

Figure 7: Inside view of the test vehicle

Figure 6: Wheel approaching just before contact Figure 6 shows the "approaching" wheel just before it contacts the windshield, recorded with a forward-facing video camera mounted in the rear of the test vehicle.

Figure 7 shows what happened inside the test vehicle at the crash test. The purpose of the test was of course to show that a driver would have been just as injured if he or she had been sitting in the test vehicle during the test as the plaintiff was at the accident that led to this law suit. The photo shows the opposite. A close look at the picture will tell observant readers (and probably many jurors at a trial) that what came down to the top of the headrests at the test is not the roof of the vehicle! It is the headliner, which is

The defendant carmaker's attorneys realized that they could not show the video to jurors and having the plaintiff's attorney pointing out that what they see is a sheet of soft plastic, not the roof of the car. The carmaker therefore did not get any use for the video. Because of this, it may be of particular interest, especially from legal point of view, that the video actually came to be used some time later at a trial in another case involving a different carmaker and different legal teams. That case concerned a young mother driving a minivan who was killed when the roof of her minivan was hit by a dislodged car wheel.

Closing speed always equals closing speed, or does it?

The reason the described test was set up with a test car moving at 80 mph striking a stationary, suspended wheel was obviously to simulate the defendant carmaker's expert witness version of the accident, which was that the injured lady's car and the bouncing, dislodged wheel both moved at 40 mph and in exactly the opposite directions. Thus the closing speed of the car and the wheel were in both cases 80 mph. But a question can be asked, which is whether or not it makes a difference how fast each is moving as long as the closing speed is 80 mph?

The answer is that it does make a difference! If we look back at Photo 1 and imagine that the test vehicle is station-

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ary and the wheel is coming from the right at 80 mph, the impact would have been the same only if the wheel is not rotating. But a dislodged wheel coming down the highway at 80 mph is rotating with a peripheral speed of 80 mph.

This means a rotational speed of more than 1,100 rpm for a wheel with a diameter of 24 inches, as is the approximate diameter of many car wheels. We can therefore also imagine what would have happened if the wheel, as seen in Photo 1, had been suspended in the same place but in such a way that it had been rotating counterclockwise (as seen in the photo) at 1,100 rpm at the moment it was hit by the test car. The rotating wheel would have had a substantial kinetic energy that would have assisted the wheel in climbing the roof. The impact would therefore have been different!

The most obvious and undisputable scenario where the closing speed cannot be used as determination of the severity of a collision is when two similar cars collide in an intersection, with one car broadsiding the other. If one of the cars is moving at 10 mph and the other 30 mph, the closing speed is 31.6 mph regardless of which car is which.

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But the severity of the collision will be far worse if the faster of the cars is the one that hits the side of the other car.

A horizontal force of 6,000 pounds on the front edge of a car roof is a high force. Or is it? The defendant carmaker's legal team no doubt hoped that the plaintiff's attorney would drop the carmaker as a defendant in his law suit when the defense expert presented his calculations that "showed" that the wheel that hit the roof of the plaintiff's car exerted a force well over 6,000 pounds on the front edge of the roof. It is therefore of interest that the testing laboratory (even though the test was done without dummies in the front seats) installed accelerometers in the test car. Based on the recorded impulse accelerations and the stated weight of the test vehicle, the peak force acting on the front edge of the roof calculates to more than 10,000 pounds! But then, as pointed out by IIHS, cars are different.

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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dy

tu S e

Cas

Who hit whom first?

w. R. Rusty Haight

Collision Safety Institute

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he "who hit whom first?" multiple car in-line collision is one crash investigators see virtually every day. We may be interested in the order of the impacts or we may be interested in the impact speeds of the involved vehicles or the severity of each impact as it may relate to the occupants of the vehicles; there may be a number of areas of interest in this type of collision. This case problem comes from a crash test involving three cars and creates just that scenario such that the potential solutions offer ways some of CDR Tool retrieved data may be useful in an analysis.

With the ever increasing availability of Event Data Recorder (EDR) data, this case problem includes Airbag Control Module (ACM) data as well as Powertrain Control Module (PCM) data from one of the involved vehicles as a focal area of the problem statement and exercise: a 2006 Ford Crown Vic. There is also a limited amount of data from one of the other vehicles - a 2000 Chevy Malibu - for use in this analysis. The data from the Malibu has been intentionally presented in such a way in the case problem statement as to add an additional area of focus to the case problem and later suggested solution(s). Data from both has been edited to fit the format of Collision Magazine.

The involved vehicles are, from "front to rear" in the line of cars: 1999 Nissan Maxima, 2000 Chevy Malibu, and a 2006 Ford Crown Vic. Photos of the involved vehicles at rest, after the collision, accompany this narrative. The case problem will focus, in part, on evaluating the data from the Crown Vic and the Malibu to see if we can sort out whether or not the Malibu was stopped when hit by the Crown Vic. For this problem, you may assume that the cars have been weighed and the weights presented are accurate at: 1999 Nissan Maxima: 3100lbs (1406kg), 2000 Chevy Malibu 3150lbs (1428kg), and a 2006 Ford Crown Vic 4600lb (2086kg). There was no pre-existing front or rear damage on the Malibu, no rear damage on the Maxima and no front damage to the Crown Vic. As seen in the photos accompanying this problem statement, the rear of the Maxima has relatively minor visible damage. Similarly, the front of the Malibu has rela-

44 Collision Magazine - Volume 10 Issue 2

tively minor damage and there is, outwardly, moderate damage to the rear of the Malibu. You may assume that all damage front or rear on the involved vehicles is/was associated with this crash test/case problem. The cars, at rest as seen in the photos accompanying the problem statement, are essentially lined up as described from "front-to-rear" with the Maxima at a slight angle to the other two. There is a slight "downhill" grade to the road relative to the apparent direction of travel of the involved vehicles. We are lead to believe, and will agree for this problem, that the Maxima was stopped prior to this event and we have no reason to believe that any of the other vehicles might have been "backing," yawing or coming into the crash in any other way that essentially "straight ahead."

We are provided with the data from an imaging/download of the Crown Vic's ACM as well as its PCM as found with this problem statement. The data is presented here, translated/reported in the latest version of the CDR program (at this writing, CDR version 16.2.1).

We are also provided with a imaging report from the Malibu; however, for the initial discussion/problem statement, it is provided in CDR version 11.0.

Given what we are provided with, for this exercise, one should want to know: 1. Is there sufficient information available here to evaluate whether or not the Malibu hit the stopped Maxima first or did the Crown Vic hit the Malibu and push it into the Maxima?

2. Assuming the answer to question #1 is "yes," describe the order of the impact(s); who hit whom first?

3. Given the order of impact(s) you have decided on, what are the speeds of the various vehicles at impact(s) (again, assuming the Maxima is stopped as the events unfold)?

4. We find the equivalent of "pre-crash" data in the Ford PCM data set. Does the speed shown at "time zero" in the Ford PCM data set represent the speed of the Crown Vic at impact?

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The cars involved in this case problem/crash test as they were immediately post-impact

On the left, the rear of the Maxima, on the right, the front of the Malibu as they were immediately post-impact

On the right the rear of the Malibu, on the left the front of the Crown Vic as they were immediately post-impact

The front of the Crown Vic as it was immediately post-impact www.collisionpublishing.com

On the left, the rear of the Malibu, on the right the front of the Crown Vic as they were immediately post-impact Collision Magazine - Volume 10 Issue 2 45

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A Review of the Development and Valida tion of Technology for Vehicular Accident Reco Simulation nstruction Brian G McHenry

McHenry Software, Inc.

A

bstract The principals at McHenry Software, Inc. (MSI), Raymond & Brian McHenry, have been active in the field of highway safety research for a combined period of over 90 years.

In that time we have performed research for NHTSA, FHWA, NTSB, public and private research organizations and MSI internal development through which we have invented, developed and enhanced the state-of-the art of collision and rollover simulation programs and other reconstruction tools. The programs include CRASH, SMAC and HVOSM which are acronyms for accident simulation and collision reconstruction programs invented by Ray McHenry while at Calspan and on which Ray and I have extensively researched, developed and published. These programs form the foundation for many of the 62

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accident reconstruction and simulation tools in widespread use today.

This paper includes a background on these programs including some of their unique validations and enhancements as well as more recent validation and correlation tests of the three-dimensional next generation combination of these programs: msmac3D. A discussion is included of what constitutes a proper demonstration of the validity of simulation and other reconstruction programs used for motor vehicle collisions, rollovers and other types of highway accidents.

I

ntroduction Today, on a PC or MAC laptop or desktop, anyone can easily and affordably make 3 Dimensional animations which can rival Hollywood's best. With these phenomenal capabilities

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pert, with impeccable and impressive academic credentials, counters that argument and supplements it with a realistic looking movie of a pig flying, how will the jury avoid being swayed to believe something that we all know is obviously not true.

For a sample of proof that 'pigs fly' take a look at an example from a Time Warner Cable Commercial "Pigs", animated by la maison, directed by Bruno Aveillan [1] In consideration of this extreme example you might begin to understand the possible problems which can occur with improper application of simulation and/or animation. The field of accident reconstruction has an increasing use (and abuse) of animation techniques for demonstrative evidence purposes. at your fingertips, it can be very easy to make anything imagined look very realistic.

For example, a sample application which is included with some animation software is how to make a cow fly! By following the example, anyone can create an animation of a cow flying. And very quickly and easily someone could create a very realistic animation such that upon viewing the animation you might believe that cows do indeed fly!

So let us take this a bit further: Let's substitute a pig for the cow. And let's say there is a court hearing to determine whether pigs fly. And at the court, an expert with an advanced degree from an esteemed university and years of experience testifies that 'pigs can fly!' and as part of the testimony the expert offers a video to prove it! Well everyone knows that pigs don't fly, yet, when the exwww.collisionpublishing.com

So you may find yourself, either as an expert or attorney or defendant, being faced with an accident reconstruction supplemented with a very realistic looking 3-dimensional animation.

This paper includes an overview of the origins and capabilities of some of the more widely used computer simulation programs for the reconstruction of collision and rollover accidents in highway safety and litigation. There is also a discussion of some of the comprehensive validations and correlations of these programs and a presentation of some of the updates and enhancements by McHenry which have evolved over the years. This information is then used to provide a baseline to assist in understanding and Collision Magazine - Volume 10 Issue 2 63

form the most accurate and reliable reconstruction for the data collected and time available."

"As scientists, engineers and accident reconstructionists, we should not let the unlimited possibilities of making anything look real (with animation) obscure our duty to perform a careful and detailed engineering analysis while also continually testing and evaluating the applied techniques, including computer programs, to achieve the most accurate reconstruction possible."

Figure 1: A flying pig from a Time Warner Cable commercial identifying what constitutes proper validation and correlation and how to identify when an expert might be using technology to try to make pigs fly!

Quotes Related To Accident Reconstruction: "Starting with a conclusion is a common way to take a wrong turn in accident reconstruction".

"The fact that one believes strongly in an opinion is no proof of its validity" J. Stannard Baker, Traffic Accident Reconstruction, Northwestern University Traffic Institute, 1990 "The process of reconstructing a collision must be seen as first collecting evidence from the accident site, police report and photos, next obtaining from data sources the available vehicle specifications, and then choosing a collision reconstruction technique to per-

McHenry & McHenry, McHenry Accident Reconstruction, 2008 , McHenry Software

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ackground On Accident Reconstruction The process of reconstructing a motor vehicle collision involves collecting all available information about the interaction of the vehicles including vehicle trajectory information, damage information, vehicle specifications and scene information.

The trajectory information is gathered based upon the police measurements, photographs and scene evidence documentation (skid marks, gouges, etc.). To characterize the interaction of the vehicles, the approximate location of the area of impact, the measured positions and heading angles at rest and any skid and gouge marks should be memorialized. Technological advances in survey and measurement equipment have made equipment available to police and investigators which can quickly, efficiently and accurately memorialize vehicle accident scenes (Figure 2). There has also been work on standards for the preservation of evidence like SAE J1674 [2]

Figure 2: Sample scene diagram and vehicle damage measurements 64 Collision Magazine - Volume 10 Issue 2

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The damage information includes measured dimensions of the damage locations and extents (Figure 2). The standard procedure by which damage is characterized is the Collision Deformation Classification (CDC, [3]) and the Equidistant Crush Measurement (ECM, [4]). There have also been advances made in the detailed measurements of the damage to vehicles through survey or laser measurement techniques.

Next comes collecting vehicle specifications and scene information (roadway layout and topography) which completes the required data to permit the performance of an accident reconstruction.

Collision reconstruction techniques are then applied to make a preliminary determination of the impact speeds and impact speed changes (ΔV's). There are two basic reconstruction techniques utilized for performing a preliminary accident reconstruction: Trajectory analysis techniques and damage analysis techniques.

Trajectory analysis techniques are mainly based on applications of the principle of Conservation of Linear Momentum and sometimes include consideration of the principle of Conservation of Angular Momentum. These techniques frequently include the simplifying assumptions of instantaneous exchange of momentum, no consideration for tire-to-ground "external" forces during the collision, and straight line travel from separation to rest [5, p84-85].

Damage analysis techniques are generally based upon the work of Emori [6], Campbell [7] and the CRASH damage algorithms [8]. These damage analysis algorithms are predominantly based on measurements from crash tests against rigid, fixed barriers without consideration of restitution effects. As a result of the lack of distinction in the damage analysis techniques between stiffness and restitution effects, the "virtual" crush model can result in substantially different vehicles sharing nearly equal slopes and intercepts in CRASH type plots of the approach period speed-change as a function of residual crush. Such similarities can occur even though the actual exposure severity for a given residual crush may be significantly different [9].

In recognition of the generally simplistic nature of trajectory and damage analysis techniques, NHTSA sponsored research projects to develop computer programs that would achieve improved uniformity, as well as improvements in accuracy and detail, in the interpretation of physical evidence in highway accidents. One resulting computer program was the Simulation Model of Automobile Collisions (SMAC, [10]. SMAC is a time-domain mathematical model in which the vehicles are represented by differential equations derived from Newtonian mechanics combined

with empirical relationships for some components (i.e.: crush properties, tires) that are solved at successive time increments by integration.

The SMAC computer model is an "open-form" of reconstruction procedure wherein the user specifies the dimensional, inertial, crush and tire properties of the vehicles, the initial speeds, heading angles and driver-control inputs. The program, through step-wise integration of the equations of motion, produces detailed time-histories of the vehicle trajectories including the collision responses.

The user compares the SMAC-predicted trajectories, rest positions and headings and collision deformations with the measured physical evidence to determine the degree of correlation. Iterative runs can then be performed, varying initial speeds, heading angles, control inputs and damage effects until an acceptable overall match of the physical evidence is achieved.

McHenry SMAC (mSMAC) is the result of modification, refinement and extensions of SMAC by McHenry Software: •

1987: include options for steer degree-of-freedom, snag impulsive restraints, articulated vehicles and trailers, polygon friction zones, variation of individual tires friction [11]

•

1997: include ability to modify the center of the collision interface (CCI), collision type specifications, supplemental impulsive restraint refinements, and proximity and detection logic [12]

•

2003: provide for the automatic iteration/optimization of the solution procedure [13]

SMAC in its original form is 2 dimensional – it works on a flat plane and omits pitch and roll responses - which is sufficient for many accidents. However in order to move SMAC to 3 Dimensions (3D) a little background on 3D simulation technology will be presented.

S

ingle Vehicle 3D Simulations One of the first 3D vehicle dynamics simulations specifically developed for highway safety applications was the Highway Vehicle Object Simulation Model (HVOSM) [14]. It was developed by Raymond McHenry while at Calspan under a contract with the Federal Highway Administration (FHWA). The specific objectives of HVOSM were to develop a means of:

1. Evaluating highway and roadside geometrics from the viewpoint of vehicle controllability, for a range of highway vehicles, and of

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2. Analyzing vehicle responses and thereby occupant exposures, in contacts and/or collisions with roadside obstacles and structures.

Since the HVOSM (Figure 3) was developed on an early digital mainframe computer, it included many analytical simplifications of the non-linearities (e.g., symmetrical suspension travel stops) aimed at reducing the complexity and the memory requirements. However, the extent of the achieved correlation with 3D test data was found to be very good. In fact, the validity of the predicted vehicle responses was good enough to lead to a small spin-off project called the "spiral jump" stunt of James Bond notoriety [15]. The stunt served as an attention-getting means of demonstrating the 3D generality and validity of the HVOSM vehicle dynamics simulation. It was also aimed at enhancing Calspan's competitive position for future vehicle dynamics simulation research contracts.

Some corrections of the preliminary estimates of tire properties were indicated and adopted in early applications. For example, early predictions of vehicle responses in contacts with a New Jersey type of barrier (Figure 4) included excessive climbing up the barrier. Since that response was clearly produced by excessive camber thrust at very large effective camber angles and loading, the estimated large-angle camber thrust properties were reduced to achieve more realistic climbing.

In the initial evaluations of validity, tire test data and the corresponding actual tires were provided by General Motors. The vehicle inertial and suspension properties were measured and provided by the Ford Motor company. The test vehicles were several used 1963 Ford police vehicles in which an instrumentation package was installed in place of the rear seat (Figure 6). The tests started out with relatively mild maneuvers (e.g., sinusoidal steer responses) that progressively became more violent (e.g., traversal of small ramps, skidding on wet pavement (Figure 5 and Figure 7). Drivers from a travelling auto thrill show were employed for the large disturbance tests which included a ramp-to-ramp jump. Detailed comparisons were made of the HVOSM-predicted responses with the measured tests results [16].

T Figure 3: HVOSM Mathematical Model

H

VOSM 3D Simulation Development An early analytical task in the development of the equations of motion for HVOSM was definition of an indexing system for the angular coordinates such that unlimited yaw, pitch and roll angles could be accommodated without trigonometric problems.

Since the predominant non-collision forces would occur at the tires, attention was focused on the simulated properties of tires as a function of loading, terrain surface properties, and angular conditions of operation. Because of limits on the available ranges of measurements of tire properties, it was necessary to estimate behavior beyond the measured ranges. Empirical fits were adopted to match both the measured ranges of behavior and the estimated behavior beyond available measurements.

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he Spiral Jump Validation Of Hvosm The testing experience with the thrill-show drivers, combined with both the degree of success with HVOSM and the existing competition for related research contracts, led to a lunch time discussion of a small project to further demonstrate Calspan's vehicle dynamics capabilities. A preliminary computer demonstration was prepared for a stunt involving a vehicle rolling over while performing a ramp-to-ramp jump, in the manner of a spiraling football (Figure 8). The demonstration wire-form video was shown to a local (Buffalo, NY) auto thrill show operator, Mr. Jay Milligan, and he agreed to fund a small feasibility study with actual measured vehicle properties and realistic speed and alignment variations.

Since Mr. Milligan was supported by American Motors, an AMC Javelin was selected for the feasibility study. Inertial measurements of the AMC vehicle were performed at the General Motors Research Center. After successful spiral jump simulation runs of the HVOSM, actual fullscale physical tests with automatic vehicle control were performed at the Calspan Automotive Test Facility (Figure 9). An anthropomorphic test dummy, representing the driver for ballast, was used in the generally successful tests.

The first public showing of the spiral jump stunt with a live driver was at the Houston Astrodome in January 1972

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Figure 4: Safety Bridge Parapet Impact at 50 MPH, 12 Degrees, Ramp-to-Ramp Jump at 44 MPH

Figure 6: Test vehicle Instrumentation

Figure 5: 1963 Ford Galaxy Test Vehicle

Figure 7: Vehicle Tests for HVOSM Validation

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(Figure 10), the ramp placement and alignment are shown in Figure 11.

The successful stunt came to the attention of the producers of the James Bond films who contracted to integrate it into the movie "The Man with the Golden Gun". For the movie, the stunt was performed "on location" in Thailand over a river (Figure 12). The ramp setup is also depicted in Figure 12. While many precautions were taken in the form of divers and emergency equipment; the movie performance was a complete success in a single take (Fig 12).

Loren "Bumps" Willert was the stunt driver for the movie and in interviews since that jump he stated that he noted that the ramps were placed out of line to compensate for the sideways travel of the car as it spiraled through the air.

(See Figure 11) "I admit it was hard to keep it on the line painted on the launch ramp, when you could see the landing ramp sitting way, way off to the side. But I did it, and the first time I did it was the take you see in the movie."

Willert drove the stunt after the movie performance 31 times in auto thrill shows and landed safely 29 times. He was reported to have said "You knew as soon as you left

Figure 8: Spiral Jump "wire-form" Computer Graphics

Figure 9: Initial Testing at Calspan Test Facility (left), Bill Milliken, Ray McHenry and Jay Milligan circa 1971 (right)

Figure 10: Performance of the Spiral Jump at Houston Astrodome 68 Collision Magazine - Volume 10 Issue 2

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the ramp whether it was going to work or not," and he also added, chuckling. "It was a spectacular stunt when it worked and a spectacular crash when it didn't."

In 1976, after 10 years of development, refinement and applications of the HVOSM by Calspan as well as other research organizations, Calspan under FHWA contract assembled a collection of all the documentation of the various developments, refinements and validations of the HVOSM [17].

Since 1978 the McHenrys, Ray & I, have performed a number research projects which included further refinements and enhancements of the HVOSM [18] under subcontracts with Jack Leisch and Associates [19], Midwest Research Institute [20], Calspan [21] and the Highway Safety Research Center of the University of North Carolina [22] as well as through internal research [23]. These refinements include driver discomfort factor monitoring,

path follower, driver model refinements, automatic terrain table generation, improvements of tire overload model, sidewall impact tire model, soft soil tire model, vehicle structure rollover modeling and other enhancements and refinements.

E

arly Validations Of Collision Reconstruction In the early 1970's, the reconstruction of vehicle-tovehicle collisions for purposes of law enforcement, statistical research and crash injury investigations was predominantly based on highly simplified damage interpretations and linear momentum calculations. Raymond R. McHenry proposed to the National Highway Traffic Safety Administration (NHTSA) that improved uniformity and accuracy of evidence interpretations could be achieved by means of a vehicle dynamics simulation approach. The simulation would be used to test approximated impact conditions by generating simulation predicted evidence in the form of corresponding rest positions and

Figure 11: 1974 Houston Astrodome setup (left), Driver's view approaching takeoff ramps (right)

Figure 12: Stunt over water from the James Bond Movie (left) and the ramps setup in Thailand for the Movie (right) www.collisionpublishing.com

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Figure 13: Sample of SMAC Collision Reconstruction

Figure 14: Example of staged crash tests, RICSAC Test 2 (left), Summary of Full-Scale RICSAC tests headings, and damage extents and profiles for comparison (Ref 25, 26) (right) with the actual physical evidence. An acceptable match of the overall evidence would be interpreted as indicating reasonably correct impact conditions [24]. A small contract was received by Calspan which led to development of the two-dimensional (2D) Simulation of Automobile Collisions (SMAC) program (Figure 13).

A series of staged vehicle-to-vehicle collisions were performed by Calspan to provide a basis for evaluating the validity of the SMAC and other simulation programs [25, 26, Figure 14]. The Research Input for Computer Simulation of Automobile Collisions (RICSAC) tests were specifically designed to serve as standards for such comparisons and were successfully used for comparison validation purposes for the CRASH and SMAC computer programs.

Unfortunately, decades later in some studies related to evaluating the correlation of PC versions of SMAC and other accident reconstruction computer programs with the RICSAC test results, there have been various levels of interpretation and acceptance of the measured results. Questions were raised as to the accuracy and validity of some of the reported RICSAC test results.

Since there was no consensus on the interpretation of some of the results of the RICSAC tests, Ray and I performed an intensive independent effort toward achieving proper and generally acceptable interpretations of the RICSAC test data [27]. The conclusions of that research were that (1) the RICSAC data are accurate and are suitable for their intended purpose of testing the validity of reconstruction techniques, (2) previously reported findings of gross errors and violations of Newton's laws in the reported RICSAC data are erroneous and (3) the SMAC program once again demonstrated excellent correlation with properly analyzed full-scale test results.

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The SMAC program has been demonstrated to correlate reasonably well with a large number of full-scale test results [28, 26, 27, and 13]. It has been generally accepted that, in the absence of significant external forces, the SMAC program correctly conserves linear and angular momentum of the two-body system. For example Figure 15 and Figure 16 demonstrate comparisons of the linear and angular momentum and kinetic energy for two different RICSAC test configurations simulated on test track friction (mu=0.87) and on a frictionless surface (mu=0.0).

The generally impressive validation results have led to a widespread adoption of the SMAC type of digital simulation approach for reconstructing highway collisions.

M

SMAC3D: Combining 3D HVOSM with SMAC In many vehicle-to-vehicle collisions the terrain is not flat and the vehicle responses may include significant pitching and/or rolling, including actual rollovers.

Since the HVOSM includes unlimited angular responses in single vehicle accidents, it became attractive to consider the development of a SMAC type of vehicle-to-vehicle collision simulation using 3D vehicles as defined in HVOSM.

At McHenry Software our first development and application of such a 3D simulation program for accident reconstruction was in 1998 when I was hired by CBS News to reconstruct the Princess Diana accident in Paris (Figure 17). Subsequent to that time we have continued to develop, generalize and further validate the corresponding mSMAC3D computer program (Figure 18).

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Figure 15: RICSAC Test 9: Comparison of SMAC generated System Momentum and Kinetic Energy time histories for Test Track Friction and No Friction; note impact at 0.10 sec, separation at 0.208 sec (Veh#1 1975 Honda Civic,2270 lbs, Veh#2, 1974 Ford Torino, 4930 lbs)

Figure 16: RICSAC Test 1- Comparison of SMAC generated System Momentum and Kinetic Energy time histories for Test Track Friction and No Friction; note impact at 0.18 sec, separation at 0.343 sec (Veh#1 1974 Chevrolet Chevelle, 4650 lbs, Veh#2 1974 Ford Pinto, 3110 lbs) An obstacle to detailed and scientific simulations in the past has been the complexity of highway vehicles (3D inertial properties, suspensions, tires, structural crush behavior) and the highway environment (geometrics, topography, obstacles). Proprietary extensions of mSMAC3D include routines that automatically generate default approximations for the required three-dimensional vehicle inputs (i.e.: suspension & tire properties, roll and pitch moments of inertia) from the plane-motion 2D SMAC simulation inputs combined with the vehicle make, model and loading. These initial input approximations combined with the

ability to overwrite any inputs for which measured values are available provides a comprehensive and efficient setup to evaluate motor vehicle collisions.

Msmac3D has achieved impressive accuracy in the general case using the option for automatically approximated 3D vehicle properties as it provides impact speed and impact speed change results that correlate within +/- 10% of the corresponding measurements in more than 25 instrumented collision and rollover tests [29].

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The following are some comparisons of msmac3D time history predictions with the full scale test measurements.

These comparisons demonstrate good correlation of the predicted roll, pitch and yaw responses with some rollover and RICSAC full scale test measurements (Figures 19, 20 & 21).

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utomatic Iteration/Optimization Of SMAC As any SMAC user is aware, many iterations of the SMAC program may be required to go from an initial approximation to an acceptable match of the measured trajectory and damage targets. Throughout the iterative process, the impact speeds and impact speed change results may change by relatively small amounts.

Also, what constitutes an acceptable match can vary widely among users. Sometimes the focus is on a detailed match of the positions of rest; sometimes the focus is on a match of damage locations and extents on the vehicles.

There is currently no standardized measure of the correlation of simulation results with the accident evidence.

Figure 17: Princess Diana Accident Reconstruction with msmac3D

Figure 18: stills from composite msmac3D comparisons with full scale tests

Since the initial development of the SMAC program, there has existed a need to simplify the application process. The ultimate simplification would entail an automatic iteration procedure. Optimization techniques applied to the evidence match can serve to reduce the required time and effort and, also, can achieve greater uniformity of the evidence interpretation.

The working hypothesis of the research related to SMAC2003: The Automatic Iteration of SMAC [13]

Figure 19: Sample comparison of msmac3D simulation rollover responses with measured data from a full scale rollover test [30] NOTE: Blue Solid lines are full scale test measurements and orange dashed lines are msmac3D simulation results 72

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Figure 20: Time history msmac3D simulation results compared with full scale test measurements for RICSAC Tests 2, 5, 6 & 9 [25]. Blue solid lines are full scale crash tests measurements, red dashed lines are msmac3D results

Figure 21: Time history msmac3D simulation results compered with full scale test measurements for RICSAC Tests 8 & 1 [25]. Blue solid lines are full scale crash tests measurements, red dashed lines are msmac3D results was that a unique set of impact conditions is required to achieve an acceptable match of all of the documented evidence (both damage and trajectory). The use of quantitative measures of the overall "fit" to the documented evidence and applications to experimental crash tests provided a means of proving the hypothesis, as well as demonstrating reconstruction accuracy and convergence rates.

Note that successful efforts on automatic iteration/opti-

mization with the msmac2D program have been reported [13], (Figure 22 -Figure 25). That research proved the feasibility of an automated procedure for achieving a "best match" of measured evidence, starting with CRASH-type linear momentum/damage analysis initial approximations.

Small deviations from a perfect "match" are, of course, imposed by the existing limitations of the SMAC computer program and by any inaccuracies in the reported evidence.

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In the reported results the automatic iteration/ optimization of msmac2D was demonstrated to successfully converge toward evidence matches in a variety of impact configurations. With measured evidence from full-scale tests on flat surfaces the automatic iteration procedure of msmac2D produced correlation of impact speeds and impact speed changes with deviations in individual results of less than approximately ± 12%.

Figure 22: RICSAC Test#8 msmac Automatic Iteration/ Optimization solution results starting with CRASH3 estimated speeds (left) and final msmac solution (right)

The research confirmed that the initial approximation of the CRASH program, or any proper combination of linear momentum and damage analysis solution procedures, should provide an adequate initial approximation from which an automatic iterative/optimization procedure of msmac2D can be used to determine the impact speeds and impact speed changes.

For 3D simulations the only change required to add automatic iteration/optimization was to simply have the control algorithms use the m-smac3D program instead of msmac2D program. The definition of the physical evidence targets for the iteration/optimization is the same for 2D or 3D except the 3D solution can also include any measured terrain elevations and slopes. The msmac3D automatic iteration/optimization program is currently in final testing and will soon be included in msmac3D.

Figure 23: RICSAC Test#2 msmac Automatic iteration/optimization results starting with CRASH3 estimated speeds (left) and final solution (right)

The msmac3D setup automatically generates all required 3D vehicle input definitions based on the vehicle make/model and any other supplemental information and the user can easily include 3D terrain information if applicable. Upcoming extensions of the automatic iteration/ optimization routine will be to include comparisons with measured tire mark information and an extension to iterate/optimize on rollover and other single vehicle accidents.

A Figure 24: Published preliminary msmac Automatic Iteration Results for RICSAC and other tests for Impact Speed Change [13] 74

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dvanced Measurement Technology There have been many major advances in the measurement equipment and techniques for documenting highway accidents. However the benefits in terms of improved accident reconstruction techniques which make use of the more detailed information have not yet been fully exploited. For example, there is an increased use of detailed scanning of vehicle damage areas to refine that aspect of the overall results of the reconstruction

technique. These more precise measurements currently can be used only in conjunction with the simplified linear assumptions of the CRASH type damage analysis techniques [31, 9]. There have been no validated enhancements to the damage analysis techniques to benefit from the use of more precise damage measurements.

Some companies are selling the ability to have "zoned" crush areas and an ability to specify more than the simplified 6 point damage protocol measurements however there have been no corresponding demonstrations of any benefits or validations of these enhancements. All damage analysis techniques are limited by the available crash tests data. The NHTSA crash test database measurements utilize the protocol of a simple 2, 4 or 6 point measurement of damage areas. And with the existing crash tests database field tests have shown that simple ruler measurements provide the same accuracy as more sophisticated measurement techniques due to limitations of the existing damage analysis techniques and the underlying data.

In the past, attention for accident reconstruction was focused on simplistic, piecemeal, stepby-step calculations within the overall event. For example the use of linear momentum, planar impact mechanics and damage analysis. These simplified first approximation techniques can help you get started with your investigation and reconstruction of an accident, however you should be sure to test and check any results with other more sophisticated and validated techniques. Wrapping all these simplified piecemeal techniques in a sophisticated graphics package may make for a good sales pitch however it is merely the equivalence of applying shine or trying to make a pig fly.

Many of the simplified accident reconstruction techniques are extremely sensitive due to many arbitrary and subjective inputs. With present day computer capabilities and speeds, 3D computer simulations offer the potential for detailed and scientific "experiments" with speeds and operating conditions on a given 3D terrain topography to match the measured evidence as a test and refinement of any preliminary results obtained with one of the simplified techniques.

Figure 25: Published preliminary msmac Automatic Iteration/Optimization results for RICSAC and other tests for Impact velocity [13]

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urrent Validations Of Collision And Rollover Reconstruction Techniques Today with the phenomenal capacities and capabilities of computers there are many collision and rollover reconstruction computer programs available to assist and supplement in the reconstruction of accidents. In general the proof of the validity of the many techniques rests with published comparisons of results of computerized techniques with known full scale tests.

In view of the complexity of many collisions and of the corresponding need for analytical simplifications and, in some cases, approximations of input parameter data, there are clearly limitations on reconstruction accuracy. For reconstructions based on a state-of-the-art 3D computer simulation program like msmac3D, realistic goals for correlation of results for the approximations of velocity and impact velocity-change (delta-V) on 3D terrain surfaces, range from errors of approximately ±5% to ±10%, depending on the detail and quality of the measured evidence.

The following are some requirements in validation proofs which should be fully provided to substantiate any validation claims: •

For any reconstruction technique, what are ALL the inputs required?

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Complete disclosure of the input file required to reconstruct an accident event for each validation comparison

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Publication of a defined protocol for calculation of any optional or subjective inputs used in validation simulations which are not based on measureable quantities

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For example some solution procedures require user inputs for point(s) and/or plane(s) to be defined for orientation

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and application of the momentum or impulse exchange on each vehicle during a collision. Validation publications should include a definition of how these input values are objectively determined for each validation test •

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The publication and availability of validation input datasets for each computer program provides the ability for the results to be independently verified and repeated and any sensitivities to variations in the inputs determined

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Some items used and included in validation papers which should be carefully evaluated: •

Impact speed – for many techniques the impact speed is an input. If you know the answer in a validation test then using that answer (the impact speed) as an input is not a totally objective measure of the overall accuracy of the technique •

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Our research and development on solution procedures to include automatic iteration/optimization of the impact speeds is in recognition of this shortcoming and to allow for a more objective validation assessment

Combined impact speed: Quoting the accuracy of a technique as a percentage of error of the combined impact speed is misleading In most accidents you are trying to determine the speed of an individual vehicle in a specific accident. What does any error related to combining the impact speeds tell you? And how does it help you determine the accuracy of the technique for the speed of each individual vehicle?

The rationale for this was because some accidents include a vehicle at rest. Errors for vehicles at rest or very low speeds (< 5 mph) can be quoted as MPH or kph difference (see next item)

Error quoted in absolute speed: •

Except for vehicles known to be at rest or very low speeds (< 5 mph) the quoting of the error as a speed difference in mph or kph is misleading

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Certainly being within 3 mph of the truth might appear good correlation unless of course the answer is 10 mph (3 mph is a 30% error!)

Average error: averaging errors that occur in validation studies is misleading and provides no indication of the maximum possible magnitude of error of the program when applied to a specific accident

Standardized reporting of errors as a percentage of difference between measured and simulation results.

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In specific accidents the analyst is interested in the specific speeds for each vehicle

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If a technique has errors which are +50% and -50% on a particular case, and reports only the average error, they would report the error as 0%! So if you are interested in the speed of a vehicle, you could have an error as great as +/- 50%!

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The largest possible error should be reported on the basis of objective validation results. A presentation of error distribution and confidence intervals can be included to help explain and identify what types of collisions may produce the larger errors. Also a discussion of what might have caused the large error should be included If a particular reconstruction technique has better correlation/validation for some impact types: validation results can be sorted by impact type if any technique performs better in certain types of impact configurations

A few additional observations on validation to consider: 1. As part of validation demonstrations and accident reconstructions some programs allow extensive use of spline fits and elaborate computer graphics to illustrate a pre-existing opinion regarding a given accident or validation. Clear distinctions should be established between scientific and illustrative computer created videos. While knowledgeable viewers can clearly distinguish the presence or absence of realism in the resulting animations, they can be misleading to an unsophisticated viewer (i.e.: a juror) 2. A lot of linear and angular momentum tools allow you to randomly and subjectively adjust the exit angle. Returning for a moment to the early development of the CRASH trajectory solution procedure [31], there was

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a "curved path" option which was added in recognition of the fact that vehicles do not generally run in a straight line from the separation to rest.

Figure 26: CRASH3 manual [31] figure on Curved Path option Depending on the direction and amount of yaw (heading) rotation the vehicles at separation may head in a direction to the right or left of the straight line path to rest and then curve back to the position of rest. A precondition to adjusting the amount of variation of the exit angle from the collision in linear momentum should be based on facts: were there tire marks or other evidence to indicate a path which is other than straight line from impact to rest? We must be sure that we simply do not adjust the separation angles because we know the correct answer or know the answer we want. We must base things on sound scientific evidence and have a protocol so any analyst using the reconstruction tool would choose the same or close to the same exit angle.

3. Several vendors have created commercial versions from the original public domain version of the SMAC program. Some vendors have translated the original SMAC FORTRAN code to other programming languages (BASIC, C++, etc.). For any translation of the original SMAC program to another language the vendor must first demonstrate that the translated computer code produces the same results as original SMAC for the same inputs. This is an initial step required for ANY computer program if it is translated to any

other language: Are the results the same? If there are any differences can they can be identified and isolated as either a problem with the translation of the code (is some portion of the internal code not translated properly or not working properly?) or a difference/error in the input files. For SMAC an objective measure of a proper translation of the Original SMAC computer code from one language to another is to compare the simulation results of the translated SMAC code with the results of the Original SMAC code.

An analogous situation would apply if a commercial vendor decided to use the code from a public domain calculator and "translate it" to work as a web application. The first step to validate that the "translation" of the original calculator code works properly as a web application would be to compare results of each program for a set of known inputs and functions. The results of the original calculator would be carefully compared with the results of the web deployed "translated" version of the calculator for identical inputs and functions. Do the results from both calculator programs agree? If not, why not? Any difference would be a problem with the translated "web application" version of the calculator code and would need to be corrected so both program produced identical results.

The most important test that the SMAC code is working properly and providing proper results is to compare the calculated impact speed changes (delta-Vs) for the Original SMAC with the translated SMAC. When performing simulations with identical input files both the Original SMAC and the translated SMAC program must produce the same results. Original SMAC code was extensively verified for compliance with the physical laws by NHTSA and Calspan by tests such as those on zero friction shown in Figure 15 and Figure 16. So an appropriate test of a proper translation of Original SMAC to another language is to input the identical published inputs for the 12 RICSAC tests for the specific weights, impact configurations, friction and control inputs and then compare the results of each program to verify that the translated code produces identical results as Original SMAC. If the results of the translated version are different than Original SMAC then the translated version has a problem and is not in adherence to Newton's laws.

The publications from vendors as part of their conversion of SMAC to other computer languages have included tabular summaries of the Impact Speed Change (delta-V) results for 12 RICSAC Tests. A summary of the comparison of results is contained in Figure 27. Note that for perfect correlation the comparison blue and red lines should be horizontal at 0%.

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Figure 27: Comparison of Impact Speed Change (delta-V) for Original SMAC results with BRANDA SMAC (solid blue triangles & line) and BRANDX SMAC (dashed red dots and line) SMAC Programs (NOTE a perfect correlation would be the blue and red lines horizontal at 0%) For BRANDA Figure 27 includes a comparison of the published results with Original SMAC results and demonstrates generally good correlation. The maximum differences in results are +0.6% and -6.6%, and the average error is -1% (the Impact Speed can be compared however it is an input and not an objective comparison).

For BRANDX Figure 27 also includes a comparison of the published results with Original SMAC and reveals a serious problem: The calculated impact speed change (delta-V) for the 12 RICSAC tests from BRANDX version of SMAC is drastically different from the original SMAC (and also different from the BRANDA results). The maximum errors in the BRANDX Impact Speed change calculations are a maximum -50.5% and +25.2% and the average error is -19.2% (Note the difference in the average error vs. maximum errors, see discussion on prior pages).

The BRANDX translated code produces different results for the calculated impact speed change (delta-V) from the same calculations for the same inputs with Original SMAC. The authors did not indicate that they made any change to the Original SMAC code except for the translation to a different programming language. Therefore the results should be nearly identical for BRANDX and Original SMAC (like they are for BRANDA).

Is the large difference in BRANDX results due to a coding/conversion issue in the calculation procedure for the impact speed change? Or is the difference due to differences or errors in the inputs? This large difference in the BRANDX translated code when compared to the Original SMAC results demonstrates a serious problem with the BRANDX code and it brings into question any claim of validation of the BRANDX translated version of SMAC. In summary, be sure to carefully review and check any and all published validation results which claim validation and 78

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correlation with prior versions of SMAC and/or with full scale measured crash tests.

For any validation documents be sure to review and check the complete input datasets for all validations and determine whether the authors used spline fits and/or animation techniques to give the impression of better correlation with reality. Having access to the complete inputs for your reconstruction product provides you and others the opportunity to examine the final values used for all inputs, including any somewhat subjective inputs like the separation exit angle, the point and angle of momentum exchange and/or any other variable inputs which can be used to improve correlation with known results. Request and obtain any and all published guidelines for determination of the basis for any such somewhat subjective inputs to be sure the same objective guidelines can be used when the technique is applied to real world accidents.

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losing Remarks And what does this all mean? First, the utilization of any particular accident reconstruction technique should not detract from the duty of the reconstructionist to perform a careful and detailed investigation and analysis of an accident. Accident/collision reconstructionists need to use any and all techniques carefully.

We recommend that you use more than one technique to be sure you are not being led to an improper conclusion due to any weakness or sensitivity of any particular technique.

Monte Carlo and other optimization techniques are a step in the right direction to provide a way to isolate inputs which are most sensitive. And you should then use that knowledge to test what that sensitivity means to your results and conclusions.

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McHenry Software is in the business of marketing accident reconstruction and simulation software. We currently have the msmac2D and msmac3D programs available. We hope you will consider adding our programs to your accident reconstruction tool chest. Details and pricing are available on our website www.mchenrysoftware and accident reconstruction forum www.mchenrysoftware.com/board. Please contact us and let us schedule an online demonstration for you.

And finally, please approach each and every reconstruction and analysis you perform as if a life depended on it since most accidents include one or more life changing (or ending) events.

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eferences 1. "Pigs" by la maison, Bruno Aveillan, 2009: https:// www.youtube.com/watch?v=CGKrUCzdatA

2. J1674 AUG2009 Early Acquisition and Preservation of Information in a Motor Vehicle Crash

3. Collision Deformation Classification, SAE Standard J224 MAR80, Reaffirmed 2011 J224_201105 4. SAE Reconstruction Practices Committee 2009 proposal for standard J2433 "Equidistant Crush Measurement" 5. McHenry, B. G., McHenry, R. R., "CRASH-97 – Refinement of the Trajectory Solution Procedure", SAE Paper 97-0949, 1997

12. McHenry, B. G., McHenry, R. R. "SMAC-97 – Refinement of the Collision Algorithm", SAE paper 970947, 1997 13. McHenry B. G., McHenry, R. R., "SMAC2003: The Automatic Iteration of SMAC", SAE Paper 2003-010486, Detroit, 2003 14. McHenry, R. R., "An Analysis of the Dynamics of Automobiles During Simultaneous Cornering and Ride Motions", The Automobile Division, The Institution of Mechanical Engineers, London 1969 15. McHenry, R. R., "The Astro Spiral Jump – An Automobile Stunt Designed via Simulation", SAE Paper No. 760339, Detroit 1976 16. McHenry, R. R., Deleys, N.J. "Vehicle Dynamics in Single Vehicle Accidents – Validations and Extensions of a Computer Simulation, Interim Technical Report, CAL No VJ-2251-V-3, Contract No CPR-11-3988, December 1968 17. Segal, D. "Highway-Vehicle Object Simulation Model - 1976", "Volume I - User's Manual", (PB-267401), "Volume II - Programmer's Manual" (PB-267402), "Volume III - Engineering Manual - Analysis" (PB267403) , "Volume IV - Engineering Manual -Validation" (PB-267404), 1976

7. Campbell, K. L., "Energy Basis for Collision Severity", SAE Paper 74-0565, 1974

18. McHenry, B. G., "Simulation Models of Vehicle Dynamics", presented November 1987, National Conference on Future Improvements to and Supplemental Guidance for AASHTO Policy on Geometric Design of Highways and Streets; Published April 1992, Transportation Research Board Circular "Beyond the Green Book".

8. McHenry, R. R., "The CRASH Program – A simplified Collision Reconstruction Program", Proceedings of the Motor Vehicle Collision Investigation Symposium, Calspan, 1975

19. Glennon, Neuman and Leisch, "Safety and Operational Considerations for Design of Rural Highway Curves," Contract No. DOT-FH-11-9575, PB-86139 664/AS, December,1985

9. McHenry, R. R., McHenry, B. G., "Effects of Restitution in the Application of Crush Coefficients", SAE Paper 97-0960, 1997

20. Glennon, Graham, "Work Zone Design Considerations for Truck Operations and Pavement/Shoulder Drop-Offs", Contract No. DTFH61-80-CO0146, Feb. 1984

6. Emori, R. I., "Analytical Approach to Automobile Collisions", SAE paper 68-0016, 1968

10. McHenry, Raymond R., "Development of a Computer Program to Aid the Investigation of Highway Accidents", Calspan Report VJ-2979-V-1, 1971 11. McHenry, B. G., McHenry, R. R. "SMAC-87", SAE Paper 880227, 1988

21. Deleys, Parada, "Rollover Potential of Vehicles on Embankments, Sideslopes and Other Roadside Features", DTFH61-83-C-0060, August 1986., Also: SAE paper 870234, 1987 22. Council, Reinfurt, Knoblaunch, Stewart & McHenry, Safe Geometric Design for Mini Cars, DTFH61-

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84-C-067, November 1986. Also SAE paper 870233, 1987

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23. McHenry & McHenry , "HVOSM-87", SAE Technical Paper No. 880228, International Congress and Exposition, Detroit, Michigan, 1987

Printed and in computer form (CD or diskette).

(Printed output pages can be saved as text files and copied to a CD to save paper)

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24. McHenry, R. R., "Approximations of Impact Conditions via Computer Simulation", NATO Conference on Accident Investigations, Brussels, Belgium, 1973

Most of the programs include options for outputs.

The outputs should be set with ALL OPTIONS ON to obtain all the outputs and time history data, etc

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Request the program version, an indication of any add-ons or options used, etc

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If a video was created, request all the files used to create the video: CAD files, Animation files, spreadsheet files, any and all notes, etc

25. Shoemaker, N. E., Research Input for Computer Simulation of Automobile Colliisions - Staged Collisions, Vol II & Vol III, Calspan Report ZQ-6057-V-4 & V-5, Contract DOT-HS-7-01511, December 1978 26. Smith, R. A., Noga, J. T., "Examples of Staged Collisions in Accident Reconstruction", from Highway Collision Reconstruction, The American Society of Mechanical Engineers, Nov. 1980 27. McHenry, B. G, McHenry, R. R., "RICSAC-97, A Reevaluation of the Reference Set of Full Scale Crash Tests", SAE Paper No 97-061, Detroit 1997 28. Jones, I. S., Baum, A. S., "Research Input for Computer Simulation of Automobile Collisions - Volume IV - Staged Collision Reconstructions", Calspan Report ZQ-6057-V-6, Contract DOT-HS-7-01511, December 1978 29. McHenry & McHenry, "SMAC2016: Validation of the msmac3D Simulation Model", work in progress to be published 1st Quarter 2016 30. Larson, Smith, Werner and Fowler, "Vehicle Rollover Testing, Methodologies in Recreating Rollover Collisions", SAE Paper 2000-01-1641, 2000 31. "CRASH3 Technical Manual", Tsongos, N., Program Director, USDOT NHTSA, 1986

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ppendix: WHAT YOU NEED to evaluate a 3-D reconstruction/animation. If you are faced with someone using one of these tools (or others), what do you need to evaluate what they have done? These programs cost from ~$3000 up to ~$20,000, so to facilitate the evaluation of an application of any of these programs, you need to obtain complete documentation of the analysis procedure.

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Some papers written on the subject: •

SAE paper number 1999-01-0101 "Computer–Generated Trial Exhibits: A Post- Daubert Update"

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SAE paper number 940920 "Case Studies in Animation Foundation"

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SAE paper number 980018, "Documenting Scientific Visualizations and Computer Animations used in Collision Reconstruction Presentations" which includes as the Summary:

"This paper has presented a proposed standard for documenting computer generated images, animations, scientific visualizations, etc. The basic standard is that any still images or videos should be documented such that any qualified analyst can reproduce them. This is the requirement for the scientific community in general and should be adopted in the accident reconstruction community. It is important to note that this standard does not refer to any method of generating these images or videos. There is no implication that any one method or any one program is superior to others. This standard addresses only the images and videos and does not address the analysis or opinions being expressed by an analyst. However, the only way to fully understand the analysis being presented or discussed is to have the ability to duplicate the images or video being presented" mchenry@mchenrysoftware.com Brian G. McHenry McHenry Software, Inc. PO Box 1716 Cary, NC 27512 USA www.mchenrysoftware.com

Request complete inputs and outputs for any and all computer programs used

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Traffic Crash Reconstruction 3 Explore advanced concepts in momentum

February 29–March 4, 2016

(Delta-v-based calculations) , energy, and

40 ACTAR CEUs

narrow-object impacts. Learn to apply the Monte Carlo technique to reconcile contradictory data.

To register, or for more information visit nucps.northwestern.edu/collision.

Led by Northwestern’s expert faculty, this course is recommended for experienced reconstruction professionals.

ADDITIONAL SPRING 2016 COURSES

CDR Data Analyst April 4–8 Advanced Crash Reconstruction: May 16–20 Human Factors Heavy Vehicle Crash Reconstruction May 16–20 (39 ACTAR CEUs) Pedestrian Vehicle Crash Reconstruction May 23–25 (23 ACTAR CEUs)

Put Northwestern’s advanced traffic crash reconstruction courses on your calendar for 2016.

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Traffic Camera

Video Analysis Validation Adam Cybanski, BSc, IIC3 Gyro Flight & Safety Analysis

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ideo cameras are everywhere in the modern world. Every year there are more traffic accidents caught on video because of the proliferation of dash cameras, red light and security cameras, and cell phones. Most modern cities employ cameras at major intersections and on highways in order to monitor traffic conditions and document accidents. Until recently, this recorded witness video has been employed qualitatively rather than quantitatively in traffic accident reconstruction.

New technologies have made it possible to extract motion data from video of a traffic accident.

With identifiable features, a video analyst can determine the speed,

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location, heading, acceleration/deceleration, and rate of turn of a vehicle at up to 29 times a second. In addition, the same data can be obtained on other vehicles within the field of view of the camera. Whether captured from a camera mounted in a car, a traffic camera overhead, or a pedestrian holding a cell phone, analysis of video can produce detailed and useful information.

There have not been, as of yet, any published velocity comparisons between video analysis and other technologies such as GPS and radar. This test will provide quantitative insight and serve as a starting point in determining the accuracy and utility of video analysis for accident reconstruction.

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Test Plan The aim of the testing was to determine the utility of vehicle motion data extracted from witness video for crash reconstruction.

Objectives The aim will be met through the following objectives:

including vehicles, motorcycles, bicycles, pedestrians as well as the surrounding physical environment 4. Video analysis provides sufficient quantitative data on entities involved in a collision, including position, velocities, acceleration/ deceleration, orientation, rate of turn and ground track 5. Video analysis provides sufficient accuracy for motion data 6. Onsite video data collection and subsequent analysis can be conducted quickly with a reasonable amount of effort 7. Additional video analysis capability development is required

1. Assess the relevance and usefulness of video analysis as investigation tool to provide traffic accident investigation evidence 2. Validate the accuracy of velocity extraction from a traffic camera video for crash reconstruction 3. Make recommendations on future video anal- Test Site ysis testing The testing was conducted on a 250m stretch Scope of divided highway on Greenbank road at Fallowfield road, Ottawa, ON. The test runs were Testing was to determine if: conducted on a northerly track, in the right lane.

1. Video analysis sufficiently covers the physical The radar gun was employed on the northeastarea surrounding a collision ern median of the intersection, directly below the 2. Video analysis provides sufficient temporal traffic camera. resolution and endurance on motion data 3. All entities in the vicinity of the collision are captured,

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Environmental Conditions Testing was conducted in clear weather daylight conditions for this test. It was scheduled early on a Sunday morning so that the amount of automobile traffic in the area was minimal, making it easier to focus on the test vehicle.

Test Equipment: Vehicle The test vehicle was a black 2013 Hyundai Sonata, a midsized 4 door sedan, driven by the test plan project officer, Adam Cybanski. The automobile has a wheelbase of 2.795m, length of 4.820m, width of 1.835m, height of 1.470m and curb weight of 1500 kg. A dash camera was mounted low on the center of the windshield. The GPS recorders were placed loose on the dash.

Test Equipment: GPS 1 The first GPS source was a Samsung Galaxy SII cellular phone model GT-I9100M running Android 4.0.3 baseband I9100MUGLD3. The phone employed an smdk4210 board and armeabi-v7a CPU. The GPS data was recorded with Sensor Track v1.38, a free sensor data logging application that had a build date of 29 July 2014.

resolution at 30 frames/second with timestamp. Video was recorded onto an Adata 8GB Class 10 High Capacity MicroSD flash card.

Test Equipment: Traffic Camera The traffic camera was an unknown 640x480 camera on a Pelco DX8100 digital video recording system. The captured timestamped footage was examined on the Pelco DX8100 viewer software version 1.1.00.2508.

Test Concept The testing of video analysis was conducted in three phases: Phase A – Pre-Test Activities. This phase included all activities that needed to be completed before the actual testing began. It started with the release of the Test Plan and ended with the start of the testing (Phase B). It included the following: 1. Identification of individuals supporting video analysis testing 2. Obtaining and preparing dash, witness and traffic cameras in vehicle and on the road

Test Equipment: GPS 2 The second GPS source was an ASUS Google Nexus 7 tablet razor model running Android 4. 3 build JSS15R. The tablet employed a flo board and armeabi-v7a CPU. The GPS data was recorded with Sensor Track v1.38, a free sensor data logging application that had a build date of 29 July 2014.

Test Equipment: Speed Measurement Radar The speed measurement radar was a Bushnell model 101911 handheld velocity radar gun which employed an LCD screen. Specifications stated that the gun radar can measure speeds from 10 to 200 MPH to 1,500 feet with an accuracy of +/- 1 MPH and +/- 2 KPH. A KEI 35 MPH K Band calibration tuning fork, serial 31828 was employed to confirm accuracy.

Radar speed information was captured on a Canon PowerShot ELPH 300HS Point & Shoot CMOS digital camera.

Video was saved onto a Kingston 16GB Class 10 High Capacity MicroSD flash card as sequential MOV files. The video codec was H264 - MPEG-4 AVC (part10) (avc1) at 1920x1080 resolution and 23.976023 frames/second frame rate in Planar 4:2:0 YUV full scale color space.

Test Equipment: Dash Camera

3. Installing and preparing redundant GPS logging equipment in the vehicle to record speed and track 4. Preparing a calibrated radar speed gun to capture and record vehicle velocities at the test site Phase B – On-site Data Collection. This phase of testing was conducted on 27 Sep 2015 early in the morning. The intent was to capture on video a vehicle going through an intersection at various speed, and record associated GPS positions and radar data. The following tests were conducted during this phase of the Test Plan: Test 1 – Collection of GPS velocity information. This test collected position, bearing and velocity information from two independent sources inside the test vehicle Test 2 – Collection of radar velocity information. This test collected velocity information on video of a speed measurement radar gun aimed at the test vehicle Test 3 – Collection of dash camera video. This test collected dash camera video from inside the test vehicle Test 4 – Collection of traffic camera video. This test collected video from an intersection traffic camera overhead

The dash camera was a 4Sight DashCam Wee model 4SK107. It provides loop recording at up to 1440x1080P 84

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Phase C – Post-Test Video Analysis. This phase was conducted in the weeks following data collection. The intent was to evaluate data from each test to determine what information can be extracted for reconstruction. For each test run the following evaluations were conducted during this phase: 1. Variety of motion data extracted

The radar speed gun was checked and spare batteries were brought onsite so that a fresh battery could be inserted if the radar gun showed signs of low power.

The radar speed gun operator familiarized them self with the radar gun's operation and carried out practice readings on passing traffic.

Pre-Test Activities: Dash Camera

2. Physical area of the test site captured 3. Temporal resolution and endurance of data captured 4. Accuracy of data extracted

To successfully prepare for testing, in addition to a dash camera, the following was required: The dash camera was checked and batteries charged. The 12v adapter was brought onsite, so that the dash camera had a continuous source of power for the duration of the test.

5. Ease and speed of video analysis Pre-Test Activities: GPS To successfully prepare for testing, in addition to two GPS loggers, the following was required: GPS logging was practiced prior to testing in order to ensure all systems were functioning, all required support equipment would be available, and to improve handling proficiency prior to testing.

The GPS loggers were tested the day prior to the test day to ensure that they would capture the vehicle position information and that the GPS was initialized to the area of operation. There was sufficient storage available to record several hours of data.

GPS loggers were fully charged, and charging cables brought onsite. The driver was made familiar with the operation of the GPS devices.

Pre-Test Activities: Speed Measurement Radar To successfully prepare for testing, in addition to a radar speed gun, a radar calibration tuning fork and a video camera, the following was required:

Memory cards were installed, and sufficient space was made available to capture all testing. A spare card was brought for the camera to be switched between each pass if required.

The driver familiarized them self with the camera operation and produced a short checklist to ensure that the camera was configured correctly and recording during the test passes.

Pre-Test Activities: Traffic Camera To successfully prepare for testing, in addition to a mounted traffic camera, the following was required: Prior coordination with City of Ottawa was conducted to ensure that the intersection traffic camera was pointed south along Greenbank road, and that the camera was operational and recording during testing.

Data Collection: GPS

The camera was checked and batteries charged. A spare battery for the camera was brought onsite, along with the associated charger so that a fresh battery could be inserted prior to each test if needed.

GPS loggers were activated prior to testing and left on for the duration of the tests. Application data was output to a CSV file onto the phone's internal memory for maximum archive speed. Archived data included date, latitude, longitude, accuracy, altitude, time, speed, bearing, and provider at one second intervals.

Memory cards were installed, and sufficient space was available to capture all testing. A spare card was brought for the camera to be switched between each pass if required, so that the most recent video could be archived to a PC.

For each vehicle pass the driver examined the loggers to ensure they were still operating successfully. Following the testing, the GPS loggers were turned off and the log files were checked.

The camera operators familiarized them self with camera operation and produced a short checklist to ensure that the camera was configured correctly and recording during the test passes.

Data Collection: Speed Measurement Radar The radar operator was stationed at the intersection on the pedestrian island shown below in order to capture data close to the traffic camera position. The radar operator

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wore a reflective safety vest to ensure that they could be seen by traffic.

Immediately prior to testing, the calibration of the radar gun was verified with the 35 mph KEI tuning fork. The radar gun indicated a reading of 35 mph, which was recorded on video. The radar gun was then switched to the km/h mode in order to coordinate speeds among the different sources.

Figure 2: Dash camera view Dash camera timestamp was enabled, so that there was a visible time overlay in the bottom corner of the recorded video for time synchronization and frame rate validation.

The camera was plugged into the automobile cigarette lighter to ensure there would be enough power to cover the duration of testing.

Figure 1: Radar measurements at intersection The camera operator recorded vehicle passes and radar information on a point and shoot camera.

For each vehicle pass: 1. The camera operator ensured that the camera was put into the record mode 2. The radar speed gun was aimed south on Greenbank road, and activated to display instantaneous vehicle speeds 3. The camera was pointed south on Greenbank as well, positioned so that it captured the radar display and traffic approaching northwards 4. The sequence was recorded

For the duration of testing: 1. The driver ensured that the camera was put into the record mode 2. Vehicle passes were recorded 3. The camera recording was stopped rather than powered off, to ensure the last sequence was saved; and 4. The camera was turned off; 5. The original memory card was brought to the project officer for download/archiving.

Data Collection: Traffic Camera The traffic camera was oriented towards the south as shown in the image below.

5. The camera was turned off 6. Following testing, the camera operator provided the video to the project officer Data Collection: Dash Camera The dash camera was securely mounted in the center of the front windscreen, pointing straight forward with a small amount of hood visible as a reference. There was not too much hood visible in order to maximize the amount of exterior environment.

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Figure 3: Traffic camera view Camera timestamp was enabled, so that there was a visible time overlay in the corner of the recorded video for time synchronization.

For the duration of testing: 1. The sequence was recorded

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2. Original footage was collected from the City of Ottawa and provided for analysis in the original recording format as well as an .avi video file As of 29 October 15, there were 232 traffic cameras in the city of Ottawa that could be viewed online and which are continually recording imagery for use in traffic accident reconstruction. Footage from these cameras can be viewed live at http://traffic.ottawa.ca/map/ as shown in Figuge 4.

Analysis Request In accordance with Vancouver Police Department Forensic Video Best Practices 8.18, the analysis request information was as follows: 1. Case file number: N/A 2. Date of submission: 28 September 2015 3. Identity of the primary investigator: Cybanski 4. Identity of the analyst or technician: Cybanski 5. Owner and source of the video: City of Ottawa 6. Technical information about the system and capture equipment: Pelco DX8100 digital video recording system 7. Time period covered by the video; 2015/09/27 07:00:00 to 2015/09/27 07:59:59 8. Description of the events on the video: Different vehicles drive through the intersection. A black sedan can be seen going through multiple times

Figure 4: Traffic cameras in Ottawa For all sources, data was saved onto a PC, and onto a USB stick as a back-up. Original file names and formats were kept in order to avoid overwriting EXIF data.

V

ideo Analysis Examination Report 2013 Hyundai Sonata, 27 September 2015.

Greenbank Rd. at Fallowfield, Ottawa, ON: The following video analysis is based on video provided by the City of Ottawa, and was not collected by Gyro Flight & Safety Analysis. The analysis makes assumptions as outlined in the document in order to best estimate velocity parameters, and may not necessarily represent the actual parameters of the incident. The findings should be treated as opinion of the analyst based on information provided.

This report is to be used for learning and accident prevention purposes only.

Purpose of This Report The aim of this report is to determine the accuracy and utility of vehicle motion data that can be extracted from traffic camera video for crash reconstruction. This will be done by documenting the analysis procedure, and comparing the results with GPS and radar truth data.

9. Physical condition of the evidence: A DVD-RW disk containing Pelco viewer installation software, a viewer data file titled FallowfieldGreenbank _20150927_0700_0800.pns and a video file (likely produced by the full Pelco software package) titled FallowfieldGreenbank_20150927_0700_0800.avi 10. Special handling instructions: Detailed in next paragraph Specific Instructions: Gyro Flight and Safety Analysis carried out a test to analyze the City of Ottawa traffic camera video file: "FallowfieldGreenbank_20150927_0700_0800. avi" and extract any data that would be useful in a traffic accident investigation. The associated analysis and investigation report will be distributed to accident investigators so they can understand the deliverables possible and processes involved in forensic video analysis for traffic accident reconstruction.

Equipment And Software Used The analysis software used in this video analysis included: SynthEyes, Google Earth Pro, Matlab, Excel, Sketchup, Rename Master, FFMpeg, Bandicam, VLC media player.

Syntheyes (Video Analysis): SynthEyes offers a complete high-end feature set, including tracking, stabilization, motion capture, and mesh building. It handles camera tracking, object tracking, object tracking from reference mesh-

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es, camera+object tracking, multiple-shot tracking, tripod (nodal, 2.5-D) tracking, mixed tripod and translating shots, stereoscopic shots, nodal stereoscopic shots, zooms, lens distortion and light solving. It can handle shots of any resolution—DV, HD, film, IMAX, with 8-bit, 16-bit, or 32-bit float data, and can be used on shots with thousands of frames.

Google Earth Pro (Geolocation): Google Earth Pro is a 3D interactive globe that can be used to aid planning, analysis and decision making. Businesses, governments and professional users from around the world use Google Earth Pro data visualization, site planning and information sharing tools. Visual representation of cities such as Melbourne, Australia; Las Vegas, Nevada; and Cambridge, Cambridgeshire include examples of the highest resolution, at 15 cm (6 inches). Google Street View is a technology featured in Google Maps and Google Earth that provides panoramic views from positions along many streets in the world.

Incident Synopsis Traffic Camera Video Analysis Validation: A sedan was driven through an intersection multiple times at different speeds in order to compare traffic camera video analysis velocity extraction against radar and GPS sources.

A video analysis estimate suggested that a great deal of reconstruction evidence was embedded in the video footage.

Data Sources Multiple sources of vehicle velocity data were present as described in the test plan. The sedan was instrumented with two GPS loggers and a dash camera. A speed measurement radar was positioned underneath the traffic camera, and readings were captured on a video camera that also

date

h m

s

latitude

longitude

accuracy

9/27/15 7 38 24.697 45.28623562 -75.75572854 5

showed traffic at the intersection. Dash camera video was collected but not analyzed for this report.

GPS information was produced from a cell phone and tablet, both running Sensor Track logging software. The resulting CSV files were both titled " 20150927.txt ", with the cell phone data date stamped 2015-09-27 7:47 AM and the tablet data date stamped 2015-09-27 7:48 AM. Radar information was captured on a series of digital camera videos. The videos were deconstructed into individual frames and examined individually. The speeds displayed on the radar gun were transcribed into an excel file along with the frame number.

The target source for this video analysis was video from a traffic camera. The video entitled " FallowfieldGreenbank_20150927_0700_0800.avi" employed the Microsoft Video 1 (CRAM) codec at 30.303030 frames per second in 15 bits RGB color space. The size of the video file was 930 MB and had a bit rate of approximately 2200 kb/s.

Geolocation of the footage was carried out using Google Earth.

GPS 1 Analysis GPS data from the phone was imported into Excel as shown in Table 1. Other than converting speed information into km/h, no data processing was undertaken. Several plots were made of the data including ground track, bearing and speed. The bearing and speed plot (Figure 5) shows the last five passes, which can be discerned by the bearing (in blue) alternating between 335 and 155. The maximum speed for each pass can be seen as red peaks on the graph when the bearing was approximately 335. The timeframe for all plots were isolated to that of the last five passes. altitude

time

speed

62.20001221 1.44335E+12 2.5645502

bearing provider 346

gps

9/27/15 7 38 25.575 45.28625285 -75.75573885 5

62.40002441 1.44335E+12 1.3761

342

gps

9/27/15 7 38 40.61

45.28627837 -75.75576676 5

63.29998779 1.44335E+12 3.2526

341

gps

9/27/15 7 38 41.63

45.28632099 -75.75578973 5

63.40002441 1.44335E+12 6.255

338

gps

9/27/15 7 38 42.652 45.28638449 -75.7558303

5

63.5

1.44335E+12 8.694449

337

gps

9/27/15 7 38 43.579 45.28646382 -75.7558831

5

63.70001221 1.44335E+12 10.320751

337

gps

9/27/15 7 38 44.577 45.28655426 -75.7559411

5

63.70001221 1.44335E+12 11.071351

336

gps

63.90002441 1.44335E+12 11.4466505 337

gps

9/27/15 7 38 45.755 45.28664881 -75.75600271 5

Table 1: GPS 1 sample data

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Data from the cell phone showed a tight ground track, indicating sufficient accuracy.

Speeds were estimated at a maximum of 45, 55, 70, 65, and 55 km/h for the five passes.

The peaks shown when the bearing was 150 represent the velocities going in the opposite direction, returning to the start point.

GPS 2 Analysis GPS data from the tablet was also imported into Excel and processed as described above.

This ground track was less dense than the cell phone ground track, suggesting a lower accuracy of position. The bearing and speed produced comparable data to the cell phone.

In order to better view the GPS data and understand the position and velocity information, the data was imported into Google Earth and played back. This provided an excellent situational awareness on the test runs and a better understanding of the relative speeds. The animation also depicted the traffic camera location and the position of all the video analysis reference points (as shown in Figure 9). A simple recreation of the captured traffic camera video was produced by moving the viewpoint to the actual camera position during the animation.

Figure 5: GPS 1 speed and bearing

Speed Measurement Radar Analysis The radar gun was activated as the test vehicle approached the intersection, and kept pointed in the same direction until the test vehicle passed by. The readings were captured on video, as well as the vehicle itself as shown in Figure 10.

The videos showing the last five passes were identified, and these sequences were deconstructed into individual .png images. The timing for each frame was calculated by dividing the frame number by the video frame rate. The reading from the radar gun was transcribed for each frame of video, and plotted against time.

The radar velocity plots appeared relatively consistent, but were unreliable at the beginning of the run, when the vehicle was out of range, and at the end as the test vehicle drove past the intersection. In addition there was some variation during the run when the

Figure 6: GPS 1 and GPS 2 tracks www.collisionpublishing.com

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radar picked up a different vehicle. Fortunately this situation was usually identified by reviewing the video at the suspect time frames. Ignoring the interference, radar data appeared reasonable and varied by no more than 2 km/h of a mean value (Figure 10).

Traffic Camera Video Analysis Analysis Methodology

Figure 7: GPS 2 speed and bearing

In order to derive motion data for the vehicle, the exact location of the video camera was determined using prominent ground features as references. Once the camera position and orientation was solved, the test vehicle was tracked. For each frame of video, the position that the vehicle would need to be at in 3D space in order to appear in the correct place on the video, was calculated.

This data was validated through comparison of 3D wire frame animation with the original actual video. The resulting data was analyzed for velocity and ground track using derived timing information.

Frame Extraction

Figure 8: Google GPS animation

Figure 9: Camera trackers in Google 90 Collision Magazine - Volume 10 Issue 2

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The video was first decomposed into an image sequence, which was comprised of 84633 .png images. Subsets of these depicting the five passes were chosen for detailed analysis (468 images for pass 1, 530 images for pass 2, 274 images for pass 3, 503 images for pass 4, and 468 images for pass 5.

On close examination, approximately 3 images of every 4 were duplicates. The vehicle did not stop still every 1/7th of a second, then re-accelerate to highway speed. The duplicate frames were created by hardware or software as frame padding. This was done either by the Pelco digital video recording system, or more likely by the Pelco's export process to save at the video frame rate of 30 frames per second.

The duplicate frames were removed from the sequences, noted in an Excel spreadsheet, and the renumbered in order to produce a continuously numbered sequence of distinct images. This new sequence was employed in camera and object photogrammetric analysis. The number of unique images was 110

for pass 1, 122 for pass 2, 63 for pass 3, 117 for pass 4, and 110 for pass 5.

Temporal Analysis Velocity and motion extraction rely on two critical parameters: distance travelled and time. Very accurate time information is needed for each distinct frame of video. Temporal analysis assembles cues such as frame rate, on screen display, and other timing cues to derive these critical timings.

The length of the entire video in both VLC Media Player and Windows Media Player was one hour. This value matched the On Screen Display time stamp (07:59:59.906-07:00:00.015) reasonably well. Although the On Screen Display time (captured from a clock in the Pelco system) is completely independent of the video capture/ playback time (controlled by the video capture hardware on the Pelco system), the times match within one second.

Figure 10: Radar speed measurement

The 110 unique frames from pass 1 spanned a time from 07:38:42.625 to 07:38:57.828 on the On Screen Display. The 109 frames over approximately 15 seconds gave an approximate frame rate of 7.169638 (15.203/109) frames per second.

The difference in On Screen Display time between each unique image was calculated in Excel and plotted on a graph. The result showed a variability in time between each frame (Figure 12).

The images were typically taken either 0.125 or 0.141 seconds apart. On occasion, a new image was not available until 0.281 seconds later. The lack of consistency required that all time and velocity measurements use the On Screen Display time rather than the video time.

Figure 11: Radar speed results

Camera Photogrammetric Analysis The next step was to determine the position and orientation of the traffic camera for each frame of video. This was accomplished through photogrammetric analysis, or specifically matchmoving of the video. All prominent features of the first frame were identified and tracked as shown figure 13. The procedure resulted in 20 manual trackers in the video sequence. The trackers were all reviewed, and poor trackers (too far, not identifiable in Google Earth, etc) were deleted. This left 18 identifiable trackers that spanned the video.

Figure 12: Time difference between frames www.collisionpublishing.com

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The scene was photogrammetrically solved, and a fair camera solution was obtained, with a residual of 1.1324. This is the error, in pixels, between the calculated position on screen, and the actual screen position, and should be between 0.5 and 3.0. The software was instructed to account for lens distortion, and this resulted in a good solution with a residual of 0.5849 The solution produced a calculated traffic camera Field of View of 46.940 degrees and focal length of 28.665. The calculated camera location was 8.78m above the average ground level with a heading of 174.949 degrees, a pitch of 7.430 degrees down, and a tilt of 0.442 degrees to the right.

Figure 13: Camera trackers in Syntheyes

The camera data was imported into Google Earth in order to plot a calculated camera position. This location was 90 cm to the south of the camera pole position visible in Google Earth. Upon close examination of the area in Google Street View, the camera ball was extended on an arm from the pole, possibly to the south and at a distance of several feet. This information validated the good camera solution.

Moving Vehicle Photogrammetric Analysis The next step was to track the test vehicle. Initially, the vehicle was manually tracked frame by frame.

The front left corner of the vehicle was chosen as the principle tracking point, as it was discernible at a distance and easy to identify close up. The initial tracking was quite imprecise and resulted in an inconsistent velocity vector as shown in the figure 15.

Such lack of precision results in a very poor velocity accuracy as shown as in the accompanying graph (Figure 16). The reason is that in the 0.125 seconds between each frame, the vehicle would only travel approximately 1.5m. The vehicle position can be estimated to within 0.5m at best, much less when it is far from the camera. This was a 33% (0.5/1.5) error index, which is very high. As a result, the time interval resulted in significant speed error.

Figure 14: Traffic camera at intersection 92

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Accuracy of velocity video analysis appears to decrease dramatically as distance from the camera increases. Velocity is a function of time and distance.

Time is captured consistently on the On Screen Display, but distance depends on the motion of the vehicle down the video frame. When the vehicle is far away, its apparent motion is relatively slight, but much greater as it approaches the camera. This is plotted in Figure 17.

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Time is plotted on the horizontal axis, and vertical position on the vertical axis. There is little change in position at the start of the graph. This translates to poor accuracy over this time range. In order to obtain a good position difference, a large time sample is required. On the right side of the graph, a significant change in position can be seen over a relatively short change in time. This suggests that greater accuracy or a higher velocity measurement resolution is possible when the vehicle is near the intersection, and the reduced accuracy requires longer sample times when the vehicle is farther away.

In order to reduce the error, a larger time interval was required. The analysis process was repeated using an interval of 0.475 seconds (every 3 frames.) In this case, the vehicle would travel 5.5m over the course of 3 frames. This produced a 9% (0.5/5.5) error, a much better value. Finally, a 1 second interval (every 6 frames) was employed. This resulted in an error index of 5% (0.5/10.0); a graph with reasonable variation, and a relatively low number (15) of tracking points as shown in Figure 18.

With the sampling interval of 1 second, or every 6 frames, all five vehicle passes were tracked. The hand tracks displayed some lateral tracking error. The assumption was made that the vehicle track did not significantly vary left to right, and in fact followed a smooth path. By fitting the tracking information to a third order polynomial, a consistent velocity track was produced that matched the vehicle location in the video. The revised track appeared straight as seen in Figure 19.

Figure 15: Moving vehicle track (raw)

Figure 16: Velocity analysis single frame interval

Velocity and Motion Extraction The revised tracker information was projected onto an average ground plane that was placed into the scene at zero elevation. This produced ground coordinates for the front left corner of the vehicle. The distance traveled between each sample frame was divided by the On Screen Display time difference in order to calculate speed in m/s, which was converted to km/h. There was initially a large variation in velocity that quickly smoothed out as the vehicle approached the camera.

Figure 17: Time vs vertical position www.collisionpublishing.com

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This initial variation in speed was caused by the difficulty in precisely determining the location of the corner of the vehicle when it was far away. As can be seen in Figure 21, it is very difficult to precisely identify features initially, but easy at the latter stages of the sequence.

Camera and Moving Object Validation The matchmoving solve needed to be validated before velocities could be compared. In order to do this, an .obj model of the test vehicle was imported into the Syntheyes software. The vehicle was resized to the correct scale, and oriented so that it was right side up and facing forward. At this point, the model was attached to the vehicle tracker (left front corner of the vehicle).

This superimposed the 3D model over the video footage, following the vehicle tracker. Playing back the traffic camera video sequence, the model vehicle followed the real vehicle in a realistic manner. This correlation between the actual video and 3D scene were an indication that the camera solution and vehicle solution were correct.

Figure 18: Velocity analysis 3 & 6 frame

The 3D scene produced a CAD-like animation that was played back from several perspectives to ensure that it looked natural and correct. The derived vehicle motion data was archived so that velocities could be compared. The data could subsequently be used to drive a 3d model in traffic accident animation software.

Figure 19: Moving vehicle track (constrained)

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Velocity Comparisons In order to compare the velocities between the GPS, radar, and video analysis, the readings first had to be synchronized. Each method had a different timing reference (GPS time for GPS, video timing for radar, and On Screen Display for video analysis), so a distinct and identifiable portion of the vehicle ground track needed to be selected, then the timings could all be adjusted to line up. The point when the vehicle crossed the last pedestrian road marking was chosen.

For the GPS, this was identified in Google Earth as 45.288138° N, 75.756876° E. The GPS timing corresponding to passing this point was noted. For the radar, the frame number wherein the vehicle passed the pedestrian line was recorded, and the timings were adjusted to that reference. For the traffic camera, the On Screen Display timing when the vehicle passed the line was used as a reference.

Figure 20: Pass 1 velocity analysis

For each of the five passes, the GPS, radar, and traffic camera video analysis values were compared on plots. The results were as shown in Figure 24.

The results of the velocity comparisons were interesting. For each pass, analysis of the associated traffic camera video produced velocities that were comparable with the radar and GPS values, especially when the vehicle was within 4 seconds of the intersection.

On the first pass, the GPS loggers agreed that the test vehicle speed was approximately 45 km/h. The radar came active at -6 seconds, but then locked onto another vehicle travelling 50 km/h in the opposite direction. The radar matched the GPS readings starting at -5 seconds, which continued until it locked onto an automobile following the test vehicle at +1 seconds. Traffic camera video analysis varied initially, then matched the GPS and radar values within 2 km/h starting at -5 seconds until the vehicle passed the camera.

On the second pass, the GPS loggers agreed with each other as well as the radar gun with the exception of two short periods at -4 and -3 seconds when the radar appeared to lock onto vehicles travelling in the opposite direction. Traffic camera video analysis under-read the other

Figure 21: Vehicle at far and close distance sensors until the -4 second mark, where it matched with 2 km/h until the vehicle passed by.

The GPS loggers matched the radar gun values on the third pass from the time the radar was able to pick up the test vehicle (-6 seconds).

The traffic camera video analysis under-read these values until the -4 second mark, when it matched and followed the other sensors. This pass was the fastest, at 70 km/h.

GPS data on the fourth pass initially showed a difference between the two loggers, caused by the relatively low GPS sample rate of 1 Hz, and as a result different time/distance calculations. The radar data converged with the two GPS loggers at -8 seconds at just over 65 km/h. Traffic camera video analysis values again matched the other sensors at -4 seconds, under-reading prior to this point.

On the fifth and last pass, radar roughly matched the GPS logger data but had many anomalies which could be seen in the video as being the result of significant vehicle traffic during the test. Traffic camera video analysis data matched the other sensors early at -6 seconds with an average speed just above 55 km/h. The difference at -2 seconds between this source and the GPS loggers (2.5 km/h) was the www.collisionpublishing.com

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Pass

Speed Duration Distance (km/h) (s) (m)

1 2 3 4 5

44 58 68 66 57

5 4 5 4 6

61 64 94 73 95

Table 2: Estimated effective ranges Within 60m (200 feet) of the traffic camera, velocity extraction from the traffic camera video process described in this report appears to be accurate to within approximately +/- 2 km/h.

These speeds are consistent with those of GPS and radar, however the vehicle does not need to be instrumented (unlike GPS or EDR), and the source of the evidence (traffic camera video) is already being collected and archived in most cases.

Findings (Test Plan Objectives) With respect to the outlined scope from the test plan:

Figure 22 Video and 3D solution comparison

1. Video analysis in this case covered a depth of 60m and width of 30m (the width of the roadway) for a total area of 1800m2. This represents a significant area in a place where traffic crosses and interacts. There is a good probability that vehicle speeds and tracks relevant to a local accident will be captured

2. The endurance of video analysis generally depends on how quick the vehicles are moving and in which direction. Temporal sampling at 1 Hz provided good speed accuracy within this endurance. A higher resolution camera could increase the endurance and Figure 23 3D animation in side perspective temporal resolution, providing greater insight into vehicle acceleration/deceleration, greatest inaccuracy of all the passes within the 4 second timeframe but this will need to be quantified with adbefore the vehicle passed the camera. ditional testing In order to estimate the usable range, the approximate length of time (in seconds) that the traffic camera video analysis matched the other 3. Almost all entities stationary or active in the space described above could be captured velocity sources was recorded. This was multiplied by the speed in and analyzed. This includes motorcycles, biorder to produce a distance as shown in the chart below. From the cycles and pedestrians, although the smaller first pass, this equated to approximately the last 61m (200 feet) at entities would not be visible and thus able to 44 km/h. Over the five passes, the average usable range was 78m or be tracked when far from the traffic camera.

250 feet.

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3D animation. The straight track of the test vehicle did not exercise rate of turn or angular orientation, which could be included in future testing 5. The accuracy of speeds extracted from traffic camera video appeared to match GPS and radar truth data within 2 km/h

Figure 24: Velocity comparisons pass 1-5 Exact distances will have to be determined in followon testing 4. Sufficient quantitative data on vehicle position/ ground track, velocity, and acceleration/deceleration (or lack thereof ) was demonstrated, and employed in

6. Traffic camera video was available the day following the test through the city of Ottawa, showing that data collection can be quick and easy. The video analysis was conducted over several weeks, but by the end of the process, a pass was completely analyzed within several hours. An experienced and proficient video analyst could extract usable data evidence in days or even hours, providing a quick and flexible capability 7. Although this test yielded a significant amount of quantitative data, additional testing will be needed in order to provide insight into other factors such as lighting, angle of approach, type of vehicle, etc With respect to the test objectives, in the opinion of the analyst:

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1. Video analysis is a relevant and useful investigation tool 2. The accuracy of speeds extracted from traffic camera video are good enough to be used in traffic accident reconstruction, if not litigation 3. Determination of speed is a cornerstone of this type of analysis, but additional position, motion and orientation data on vehicles could also prove useful in an investigation

"

Traffic cameras are located at major intersections in today's modern cities, and there is a strong likelihood that vehicles involved in an accident will be captured on video.

Conclusions

"

Traffic cameras are located at major intersections in today's modern cities, and there is a strong likelihood that vehicles involved in an accident will be captured on video. Analysis of this video can provide evidence on the speed and ground track of vehicles which is useful for traffic accident reconstruction.

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The accuracy of velocities extracted through analysis of traffic camera video are comparable to radar and GPS sources. Unlike GPS, vehicles do not need to be instrumented in order to provide data, and coverage from traffic cameras is typically 24/7.

The addition of traffic cameras provides a capability to capture velocity evidence. Optimum placement and improved resolution increase the range of velocity capture.

Velocity extraction can be conducted within hours of receiving the traffic camera video, as long as reliable survey data or suitable Google Earth imagery is available. As a result, velocities on vehicles of interest may be derived quickly and independently from primary accident investigation, and serve as either a principle or validating source of velocity information.

Recommendations Follow-on testing should be completed on the dash camera video that was captured as part of this test. It will be useful for validation of dash camera video analysis.

Follow-on testing should be completed using the same basic methodology. Testing should include different types of traffic cameras, including those with higher resolution and lower frame rate, at night as well as day. Trucks, motorcycles, and bicycles moving away from the camera or across the field of view should be analyzed in order to get a better idea of the scope that traffic camera video analysis can cover and the associated accuracy and range.

Any other suggestions on future video analysis testing is welcomed. The author would like to thank Mrs. Julie Cybanski, Detective Alain Boucher and Mr. Motaz Aladas, without whose contributions this report would not be possible.

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udy t S Case

n o i t Solu

Who hit whom first?

w. R. Rusty Haight

Collision Safety Institute

Solution:

A

s simple as it sounds, the best place to start an analysis is by asking yourself the question: what do I have to work with? Instead of a guide ("guide" which may come from an old word meaning "crutch"), one should really start with the basics: a blank piece of paper on which you can make lists and even maybe sketches. This is usually a much better way to start if for no other reason than to push you into really evaluating the information before you and carefully reading things like CDR Tool reports as though each one is unique because ultimately, they are.

So, for this case problem, we might make a list of some of the things we know from the problem statement.

We'll be focused here on addressing the questions posed so the information in our list will be a little more narrowly focused than a list you might want to make in another situation. You may well find other information in the CDR report(s) or information you gather separately from the human, vehicle and/or environment in a given case to add to a list specific to that scenario.

What we know about the Nissan Maxima We know that the 1999 Nissan Maxima weighed 3100lbs (1406kg). We have adopted/accepted that it was stopped before any other collision(s) took place.

There is relatively minor damage to the rear of the Maxima particularly in the area where we see the Malibu at rest against the Maxima post impact.

What we know about the Chevy Malibu We know that the 2000 Chevy Malibu weighed 3150lbs (1428kg). We are trying to find out if the Malibu hit the Nissan first or it if was pushed into the Nissan by the Crown Vic. We find damage on the front of the Malibu consistent with the damage to the rear of the Nissan. On the rear of the Malibu, we see that the rear bumper is displaced "inward and downward" relative to a rear-to-front viewpoint of the car frame of reference. This is also evidenced by the inward and downward twisting of the Malibu's exhaust assembly. The

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damage to the rear of the Malibu is more significant than that on the front. The twisting of the rear bumper cover and damage appearance suggests the rearending Crown Vic "under rode" the rear of the Malibu, at least to some degree.

Based on the CDR report for the Malibu, as provided in the problem statement, we know that the airbag control module (ACM) has recorded a single Non-Deployment (ND) event. Reading the Data Limitations text in that report, we know that this module "... (SDM) can store up to one Non-Deployment Event. This event may be overwritten by another Non-Deployment Event. ..." Reading that, we notice there's no mention of the later occurring, potentially overwriting event having to be of greater severity. We see that there is a mention of "... the Time Between Events (being) displayed in seconds. If the time between the two events is greater than five seconds, "N/A" is displayed in place of the time. ..." but notice there is no data parameter displayed in the report for "time between events." We also see, in the Data Limitations text, as we see in most, that the "precrash data is asynchronous" and we see the source of the pre-crash data parameters identified.

In the CDR Report "System Status at Non-Deployment" data table, we learn that the driver's belt was unbuckled and if there was a passenger airbag suppression switch on the Malibu, the passenger airbag was not suppressed (and after looking in the car, we find there is no such switch). We find that the ignition cycle count for the Malibu at the time of the ND was 26,346. Notably, there is no ignition cycle count for the time of the imaging/download which, based on our training and experience, we know is normal for this type of system.

Together with other observations regarding the data, that there's a number for the ignition cycle count at the time of the event which both makes sense to the vintage of the vehicle and is not reported as "0 (cycles)" we may conclude that there was no apparent power interruption which lead to a power depletion during the data recording process; the data which the ACM held

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in RAM when it came time to record that data was apparently completely recorded.

In the CDR Report pre-crash data table we see what appears to suggest the Malibu was slowing from the "-5sec" sample frame at about 12mph to about 2mph at the "-2sec" sample frame. After that, the vehicle speed shows as "0mph" up until the recorded ND occurred "freezing" the pre-crash data buffer at "algorithm enable" for potential recording. The brake switch shows "ON" from the "-6sec" to "-3sec" data frames consistent with the vehicle slowing as suggested by the pre-crash speed data.

Looking back to the System Status at Non-Deployment data table, we find that the "Maximum SDM Algorithm Longitudinal Velocity Change (MPH)" is reported as "0.00mph." Based on our training and experience with other similar GM ACMs of this type, we know that these systems will only report a negative longitudinal "Velocity Change" (delta-V). So we can conclude that the ND was a function of a very, very minor frontal collision - one which somehow woke up the ACM but failed to reach a reportable value greater than 0.00mph for the longitudinal deltaV - or there was another reason for this event.

At this point then we're left to attempt to deduce the cause of the ND. Was it because the Malibu hit the Nissan "very gently," below the threshold where a number (other than 0.00mph) could be recorded for the "Maximum SDM Algorithm Longitudinal Velocity Change (MPH)?" If that's the case, we're left to explain how it would be that the speed, for the last two second's worth of pre-crash data samples, would be "0mph."

One might speculate, given there was braking observed ("ON") in the "Brake Switch Circuit State" portion of the pre-crash data that the Malibu was in full locked wheel braking; however, that switch status changes for the last two samples leading to the ND when the speed is reported as "0mph." Moreover, for that to be true, we'd have to evaluate the car and find it was possible to have locked wheels during braking meaning: does the car have ABS and, if so, was it working properly? Still, that doesn't take us away from the observation that the brake switch status is "OFF" for the last two samples of data before the ND. That observation would seeming run counter to the idea that the "0mph" is a function of locked wheel skidding.

The other possibility is that the Malibu was rearended by the Crown Vic, which would result in a positive longitudinal delta-V, with a magnitude not reportable by this ACM and that would then be the cause/source of the ND. Since the accelerometers in the ACM respond to both inertial motion and vibration, and given our training and expewww.collisionpublishing.com

Editor's Note: Regular readers of Collision will recognize that the Case Problem found in the last issue (Volume 10, Issue 1, "Case Study: Three Car In-line Crash Analysis with CDR Data") was also a "who hit whom?" type of crash; however, one should also recognize that the CDR data we're working with here comes from different manufacturers and, as we'll see in this solution, the analytical sequence - and the answer(s) to the question of who hit whom first - is approached somewhat differently with these cars and this data. While the type of crash is basically the same, key concepts from both this three car example and the one in the last issue can be applied in a number of scenarios. It should quickly become clear that this isn't simply some "plug and chug" approach to "testing one's skills," rather these case problems are offered to challenge the reader to look at the crashes and retrieved data in, perhaps, a new light.

Too often, we see one engaged in reconstructing a crash has the propensity to look at the problem and want to go immediately to a calculator or, worse, a formula sheet.

The truth is, trying to simply throw a "formula" at a problem is always the wrong way to start a real, solid analysis. Rather than trying to oversimplify or even dumb down the analysis with "one size fits all guides," "formula sheets" and the like, one should approach each crash analysis as though it's unique - because it is. So, in Volume 10, we've offered two 3 car in-line crashes; however, they are very different in many ways.

Just like there's no "one size fits all" definition to what "a reconstruction" is, there's no quick and easy, all-encompassing approach to analyzing a crash particularly when using CDR Tool retrieved data, or at least when using it properly. This Case Problem then, the second in a series of case problems illustrating the use of Event Data Recorder data as part of an analysis, together with the first one, should serve to reinforce that point.

Collision Magazine - Volume 10 Issue 2 103

rience with other similar modules and GM vehicles, we know that this is a possibility: that the module could "wake up" as a function of the vibration felt in a rear-to-front collision and then, because the acceleration and resultant delta-V is in the positive longitudinal direction, not record a number for the "Maximum SDM Algorithm Longitudinal Velocity Change (MPH)" other than 0.00mph.

"

The logical working theory at this point would then seem to be that the Malibu, braking pre-impact, rearended the Nissan first and then it was rearended by the Crown Vic.

The logical working theory at this point would then seem to be that the Malibu, braking pre-impact, rearended the Nissan first and then it was rearended by the Crown Vic

"

The apparent "problem" with that conclusion would be that the Malibu would then have first a negative longitudinal delta-V of something with a magnitude greater than "0mph" and the recorded ND occurring subsequently with a "0mph" delta-V. Conventional wisdom might be, given experience with other GM vehicle's ACMs and setting aside the knowledge that the various systems don't all function the same way, that it would take a delta-V of greater magnitude to overwrite another preceding event.

In fact, consistent with that notion, for some other GM systems we find this entry in the Data Limitations: "... 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 longitudinal velocity change. ..."

However, we are reminded in this case, that the Data Limitations text for this specific system tells us that the: "...(SDM) can store up to one Non-Deployment Event.

This event may be overwritten by another Non-Deployment Event. ..."

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Noticeably missing from the Data Limitations text for the specific module found in this Malibu is a requirement for a greater delta-V where a subsequently occurring event might overwrite a previous event. The inference is that the module will record the next occurring ND event, without regard to a previous event, assuming the previous event isn't locked as a function of occurring within the 5 seconds before a deployment.

We are or should be reminded here that the Data Limitations text for GM vehicles is very specific to the module type and that seemingly minor nuances in the text can have major implications and, sometimes, what it doesn't say is as important as what it does say.

At this point in the analysis, bearing in mind that we still have to consider the Crown Vic condition and data, as we look to the first two questions posed in this case problem, we might answer...

1. Is there sufficient information available here to evaluate whether or not the Malibu hit the stopped Maxima first or did the Crown Vic hit the Malibu and push it into the Maxima?

At this point, yes, based on the damage to the vehicles and the data from the Malibu alone - so far. We still have to evaluate the Crown Vic data, but at least for now, we have a working theory for the analysis but we know our working theory will have to hold up to an evaluation of the Crown Vic data.

2. Assuming the answer to question #1 is "yes," describe the order of the impact(s); who hit whom first?

Based on the damage to the vehicles and the data from the Malibu alone - so far - we can at least hypothesize that the Malibu reareneded the Nissan first and then the Malibu was rearended by the Crown Vic. What we know about the Crown Vic The 2006 Ford Crown Vic weighed 4600lb (2086kg).

There is damage to the front of the Crown Vic which is generally consistent with damage to the rear of the Malibu acting in a front-to-rear direction relative to the Crown Vic. Notably, the Crown Vic - a recently retired police vehicle - had a curved push bumper on the front at the time of the crash. We can see, in the photos taken of the Crown Vic after the crash that the push bumper, particularly on the Crown' Vic's left front, has been pushed rearward and down to a point where the top trailing edge of the push bumper is, as seen post impact, under the Crown Vic's hood.

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The Crown Vic provides the end user with data from both the car's ACM and its Powertrain Control Module (PCM). Taking a look first the ACM data, we find that the module has recorded both a "Frontal Deployment" and a "Side Trigger."

At 3.6 in the report's Data Limitations text for the Crown Vic ACM, we find: "... The "Cumulative Delta V during the algorithm run time accurately reports observed delta-V for the period the RCM's decision making algorithm runs which may, in some cases, be longer than the actual crash pulse for a given event. For that reason, the reported Delta V may be different than a reconstruction based calculated Delta V for a given event. ..." (sic) There is a significant amount of other information contained in the Data Limitations text which may be addressed separately.

In the report's System Status at Data Retrieval data table, we find the referenced "Longitudinal velocity change during algorithm run time (MPH)" and the "Algorithm run time (msec)." In this case, the "Longitudinal velocity change during algorithm run time (MPH)" is reported as -12.64mph (-20.3km/h) and the "Algorithm run time (msec)" is reported as 274.4ms.

Reviewing the first line of the "Frontal Deployment" and "Side Trigger" data tables, we see that, based on the parameter "Ignition Cycle Key On Timer at Start of Frontal Event (sec)," they both occur at 16 seconds into the current ignition cycle. Unlike the GM system module in the Malibu, this isn't a count of ignition cycles but is a time, in seconds, into the current cycle representing approximately how long the module/car had been powered up from engine start to essentially impact on the current cycle. The reported time (16sec) is very (even unrealistically) short;

however, keeping in mind this data comes from a crash test so it may not only be taken as accurate but an intentional artifact of the test as discussed later.

Focusing on the "System Status at Frontal Deployment" data table, we find that the occupant classification system or the system designed to evaluate an approximate weight in the right front seat of the Crown Vic senses something greater than about zero but doesn't sense enough weight to turn the passenger airbag "ON." For that reason, the passenger classification status indication of "Occupied Disable" tells us there was something in the right front seat or the system was out of calibration; however, the indication is that whatever was observed wasn't enough to enable or turn the passenger airbag "ON." Inasmuch as this was a crash test, we know there was a 6lb (2.7kg) equipment box in the right front seat. This weight would be greater than "zero" but less than the approximate weight of the 5th percentile female required to turn the airbag "ON" (it would then read: "Occupied Enable").

The data table tells us that the driver's belt was unbuckled but the passenger belt was buckled. It also tells us that the driver's seat was in rear two thirds of the seat track run or not forward of the "switch point."

Consistent with these indications in the report, as can be seen in the photos accompanying the solution text below, the driver's belt latch plate was intentionally buckled into the right front passenger's buckle assembly. This "fooled" the ACM into thinking the driver belt was "unbuckled" and the passenger belt was "buckled." As part of the planning of this crash test, the anticipated projected severity should have resulted in a "pretensioner only" deployment for a buckled driver (when compared to past tests conduct-

On the left, we can see the passenger belt in the retracted position. Inspection of the vehicle after the crash test shows that the belt is tight at the "D" ring and cannot be extended from the retractor consistent with the passenger belt pretensioner having been deployed while the belt was in that retracted position. On the right we see the driver belt buckled in the passenger buckle assembly. www.collisionpublishing.com

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ed with this model Crown Vic). However, with the driver reported as "unbuckled," the system should have (a) not deployed the driver pretensioner and (b) be more likely to deploy the driver's airbag - both of which are consistent with the results in this test.

In that respect then, the Frontal Deployment data table also tells us which devices were deployed as a function of this crash. While some might believe that the timing of various devices is beyond the scope of a general reconstructionist, understanding the timing of the devices sheds light not only on the behavior of the ACM but on the general severity of the crash. Generally, the earlier the deployment of a device, the more severe the crash pulse appears to the ACM. In this case, the first devices deployed were the Driver's airbag (first stage) and the passenger belt pretensioner as discussed. We also notice that the driver's belt pretensioner did NOT deploy. Based on our training and experience and an abundance of crash tests with the Crown Vic, this is an expected result where the passenger belt is "buckled."

It also reinforces the idea that the belt pretensioner deployment is not reliant on there being "a passenger" in the right front seat for this system. The timing of the deployment (pretensioner and driver airbag at 45.6ms) is consistent with the relatively lower severity of this crash from the perspective of the ACM. The Crown Vic ACM report also includes a graph and data table associated with the "Longitudinal Crash Pulse."

There is a discussion of the layout of the data table, graph, associated elements and some of its interpretation in the Data Limitations. From that, we know that the "... time series deceleration data recorded by Ford's module during a crash is mathematically integrated into a partial Delta V by the Bosch tool. ..." (sic) A translation of that passage is the crash pulse acceleration data (shown in red in the graph labeled "(VIN) Longitudinal Crash Pulse") which can and does show as captured then recorded positive acceleration as well as negative acceleration (as opposed to being simply "deceleration") and represented in the data table marked "Crash Pulse Data" under the column "Long. Acceleration" is the data recorded in the ACM. The "Long. Cumulative Delta V" in the data table and shown in blue in the graph is calculated, post processing, by the Bosch CDR Tool software following the directions of, here, Ford.

This observation is important for two reasons. First, we can see where the data is represented as a "cumulative delta-V" and, in the context of a collision, the "delta -V" represents the severity of the crash as a function of acceleration experienced by the car during the crash pulse, the calculation is demonstrably done incorrectly. It begins with the record106 Collision Magazine - Volume 10 Issue 2

ed data set, in this example, starting about 56ms before the module even "woke up" recognizing there was a crash.

The calculation incorrectly includes negative acceleration at the reported resolution of something more than 0.41g before the collision occurs. Therefore, the "delta-V" reported in this data table and shown in the graph is, for the period shown with an understanding that it is an incomplete representation of the crash pulse, over reported compared to what the car was actually "experiencing" as a function of the crash pulse alone since it takes into account pre-crash negative acceleration.

Second, we might then consider that data's usefulness in light of the other reported parameter associated with crash severity: "Longitudinal velocity change during algorithm run time (MPH)" for the period "Algorithm run time (msec)." Here, we see that the run time delta-V is reported as -12.64mph (-20.3km/h) over the run time 274ms.

We also find a lengthy discussion of the parameter at Data Limitations point 3.6 (reduced here) which includes: "... The "Cumulative Delta V during the algorithm run time accurately reports observed delta-V for the period the RCM's decision making algorithm runs which may, in some cases, be longer than the actual crash pulse for a given event. For that reason, the reported Delta V may be different than a reconstruction based calculated Delta V for a given event. ... Users should compare the reported algorithm run time to a normal crash duration of approximately 100-150 ms. If the algorithm run time is significantly longer than the reconstruction estimated crash duration, you may want to consider accounting for after contact acceleration - whether X positive or negative - where appropriate. End users using the crash pulse graph to estimate the event Delta V, should not include any speed loss accumulated as a function of braking prior to algorithm wake up in the event Delta V. ..."

Although the passage doesn't include the word "longitudinal" the description simply calling the parameter the "Cumulative Delta V" and adding the word "cumulative," in context and description we may assume the reference is actually to the parameter "Longitudinal, velocity change during algorithm run time (MPH)" and that what's being described is only for the longitudinal axis, not for both the longitudinal and lateral axes. That being said, the earlier point made that the graph and data table which does include the word "cumulative" and shows an incorrectly calculated delta-V seems to have been addressed somewhat in the Data Limitations text where we see this passage: "... This module has two different displays with Delta V information. The cumulative longitudinal Delta V shown in the system status section of the report reflects

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During the crash, the front of the Crown Vic underrode the rear of the Malibu as a function of pre-crash braking and the shape of the front of the Crown Vic including the push bumper the change in forward velocity that the sensing system experienced from the point of algorithm entry to algorithm exit. The cumulative longitudinal Delta V may understate the Delta V slightly because the algorithm does not begin until the deceleration reaches a pre-specified level of approximately 2 G's, so the first one or two milliseconds of actual Delta V may not be included in the total. ..."

So, for this analysis, which one can we use? Are either or both reliable...unreliable?

Experience tells us that actual car-to-car crashes can and often do last far longer than 100-150ms and the onset of a crash (particularly in one like this) can easily take more than "one or two milliseconds" to develop. The oversimplification/over generalization, even if leading toward an example given in the Data Limitations, shouldn't supplant reconstruction training or analytical judgement. What we can take from these passages together is that the "Longitudinal velocity change during algorithm run time (MPH)" starts at the point where the module "wakes up" "sensing" the crash which is shown as "time zero" on the "Longitudinal Crash Pulse" data table and graph, runs for the period "Algorithm run time (msec)," and that period is longer than that shown on the "Longitudinal Crash Pulse" data table and graph.

The "Longitudinal Crash Pulse" data table and graph is useful in terms of generally describing the earliest part of the crash pulse but doesn't answer the question: is the val-

ue found in the report for "Longitudinal velocity change during algorithm run time (MPH)" significantly over reported or under reported?

In this crash test, the Crown Vic struck the back of the Malibu and, as can be deduced from the damage, the push bumper shape and pre-crash braking caused the front of the Crown Vic to underride the rear of the Malibu. Not an unanticipated occurrence in the real world. The crash test photos show this occurring but an analyst, looking at the crash after the fact, would have seen the rearward and downward displacement of the push bumper of the Crown Vic and the under and forward twisting of the Malibu's rear bumper. Marks on the Malibu's rear bumper and the displacement described should lead one to conclude that the Crown Vic underrode the rear of the Malibu. In the larger scheme of things, this means the crash pulse would be relatively longer than a "bumper-to-bumper" collision or a car-to-barrier test both of which would be more in the range suggested by the Data Limitations (100-150ms).

Here, one might expect a crash duration more on the order of 200ms or more, so the run time of 274ms in the given data set isn't really out of line with an anticipated crash pulse duration. Given that this case problem comes from a documented crash test, we know that the crash pulse is actually about 230ms. That, of course, makes the "Algorithm run time (msec)" of 274ms about 44ms longer - an insignificant amount of time in the overall context here.

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However, if one were to adopt that the crash pulse, as suggested in the Data Limitations text were to be considered to be more like 100-150ms and that would lead to some recalculation of the appropriate delta-V for the crash subtracting out some negative acceleration for about 125 to 175ms we might be inclined to then incorrectly subtract as much as 3-4mph from the "Longitudinal velocity change during algorithm run time (MPH)" which would suggest artificially reducing the delta-V (based on the passage "...

If the algorithm runs for another 100 milliseconds beyond the end of the normally observed crash pulse, the data recorded may reflect an over reported event Delta V inasmuch as it includes the 2 mph from post contact braking observed while the system was still active. ...").

For this case problem analysis then, if we were to (1) correctly deduce from the damage to the involved Malibu and Crown Vic that this is an underride collision noting that (2) underrides are normally longer duration than bumperto-bumper collisions and (3) observe that the deployment timing (about 45ms after wake and perhaps as much as 70ms after the actual onset of the collision event) is consistent with a longer duration event, while (4) keeping in mind that the collision forces acting on the Crown Vic are almost exclusively on the longitudinal axis, we can adopt that the "Longitudinal velocity change during algorithm run time (MPH)" is likely reasonably representative of the actual delta-V of the Crown Vic in this case.

Having evaluated the available information then and adopting the recorded run time delta-V as being representative of that for the Crown Vic, we can turn our attention to a use for the Crown Vic's delta-V in this particular analysis. (Now, it's time to break out a calculator...)

Equation 1 Using equation 1 (the same method suggested as part of the analysis in Volume 10, issue 1's case problem), we can calculate the closing velocity for the Crown Vic to the Malibu. The problem is: is it the Malibu alone or, since we already have a working theory that the Malibu hit the Nissan first, is the Crown Vic hitting the Malibu and the Nissan together as effectively a single mass?

Using equation 1, the weights provided as part of this case problem and a range of values for restitution between 0.1 and 0.15 (based on an assessment of the damage or lack of damage to the Crown Vic and Malibu), the ACM reported delta-V of -12.64mph (-20.3km/h), and the as108 Collision Magazine - Volume 10 Issue 2

sumption that the Malibu and Nissan are together or "very close" together after the initial Malibu-Nissan crash, we would calculate a closing velocity for the Crown Vic of 19.1 to 19.9mph (30.7 to 32 km/h). This would result in a delta-V for the Malibu/Nissan of about 8-9mph (12.8 to 14.5km/h).

If, on the other hand, we were to make all the same assumptions except adopt that the Malibu was a separate mass (hadn't yet hit the Nissan), the closing velocity for the Crown Vic to the Malibu would have to be 27 to 29.6mph (43.5 to 47.6 km/h) and result in a delta-V of about 18 to 19mph (29 to 30.5km/h). But this approach produces another problem since we're still adopting a single delta-V for the Crown Vic over the 274ms period. If the Malibu was not in contact with the Nissan as it was hit by the Crown Vic, then there would have to be additional time involved getting the Malibu up to speed and into the Nissan. This would probably have also resulted in a different event (different polarity ND) recorded for the Malibu.

Of the two, which seems the more reasonable answer given what we know about the crash thus far; taking into account damage and, of course, the CDR report data for the Malibu? All together, the considerations should lead one away from concluding there was a separation of the Malibu and Nissan when the Crown Vic hit the Malibu.

Surely, we would conclude that the analysis of the ACM data from the Crown Vic supports the working theory that we developed such that the Malibu hit the Nissan first and then they were hit together by the Crown Vic. Alternatively, assume that one attempts to "reconcile" the ACM runtime delta-V to the artificial shorter period (100150ms) and then reduces the delta-V to be used in the same calculations to about 9.5mph (15.3km/h) given the example method shown in the Data Limitation. In that case, the closing velocity for the Crown Vic to the Malibu and Nissan together would be between 14 and 15 mph (21 and 22km/h) and if we were to use that methodology and assume the Malibu was hit alone, the closing velocity would have to be 20 to a little more than 22mph (about 29.8 to 32.6km/h). Even if these numbers didn't fit the underlying crash test data, they don't make sense to the rest of the data in this case which brings up the remaining PCM data available from the Crown Vic as another method of fully evaluating this crash.

But first, we might go back and revisit one of the questions posed as part of this case problem: 2. Assuming the answer to question #1 is "yes," describe the order of the impact(s); who hit whom first?

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As previously noted, based on the damage to the vehicles and the data from the Malibu, we initially hypothesized that the Malibu reareneded the Nissan first and then the Malibu was rearended by the Crown Vic. Now, with an analysis of the delta-V experienced by the Crown Vic, we have another way of supporting that initial working theory, that being that the most reasonable conclusion one can draw from all the information considered thus far is that the Crown Vic rearended the Malibu as it was in contact with the Nissan after the Malibu had rearended the Nissan first (as an aside, video of the crash test shows the two together at rest as the Crown Vic struck the rear of the Malibu).

The additional data from the Crown Vic previously mentioned, which we want to consider and include in this analysis, is that available from the PCM effectively taking the form of "pre-crash" data for the Crown Vic. The PCM data is found in the problem statement portion of this issue of Collision.

The Crown Vic Powertrain Control Module can record, in a loop, about 25seconds of 16 data elements in memory page frames captured every 0.2sec. The data takes the form, in the report, of two data tables ("PCM EDR Data (1)" and "PCM EDR Data (2)" as found earlier in this issue) which are associated to one another as a function of the "buffer address" for each set of data recorded. When the "restraint deployment signal" (RDS) is "received" by the PCM and the data can then be reordered chronologically from the receipt of that signal by the CDR Tool software, the two tables may be associated that way as well. In this case, the "RDS" was received and the data has been reordered by the CDR Tool software to show, at the top of the columns of data, the chronologically "oldest" data in the 25 second loop.

When the PCM is powered up in normal operation (when the ignition is turned to "ON"), it begins to write data to memory address EA000010 no matter where it left off when the car was shut down on the previous driving cycle.

It also begins to keep what's referred to in the Data Limitations as the "Key on Timer." The Data Limitations explains how the timer values increment relative to the actual recording rate of the PCM. When the RDS is received, the CDR Tool software uses the memory page frame where the RDS was indicated as received as a reference point then goes to the page frame immediately before that and labels that previous frame as "relative time zero." We see that in the PCM report for this case problem at buffer address EA000550 for the "time zero" point." When the PCM receives the RDS, it is supposed to continue to record data for approximately

4.6sec (23 page frames) and then stop recording new page frames of data and "lock" the data for later retrieval.

The "RDS" has been examined in crash tests conducted at the ARC-CSI Crash Conference and elsewhere and, for this analysis, we should want to know that what creates the RDS is, after the end of the ACM's algorithm run time, the ACM will essentially cut the voltage on a dedicated line it shares with the PCM for 100ms and then bring it back up which the PCM is to interpret as "RDS received."

If that doesn't happen for whatever reason, the RDS is not received and the PCM data remains volatile (unlocked).

This means, for the Crown Vic system, at a minimum, the RDS will occur 100ms after the end of the ACM's algorithm run time.

For this case problem, we know that the algorithm run time is 274ms. With the 100ms "up and down" time for the voltage signal, that means that it would be at least 374ms between the time the module "felt" the crash ("woke up") and the PCM would have received the signal. Considering the ACM crash pulse data and looking at the negative acceleration before the represented "time zero" we can reasonably add nearly another 30ms to that time, putting the number for the time between "impact" and the RDS at close to 400ms.

In practical application of the data available for this case problem, we see the RDS is received at page frame buffer address EA000560 and, if we go back two page frames (representing 200ms each or about 400ms total), we can conclude that the module would have probably "felt" the collision nearest the period when the PCM observed page frame EA00530. This brings to mind case problem question 4: 4. We find the equivalent of "pre-crash" data in the Ford PCM data set. Does the speed shown at "time zero" in the Ford PCM data set represent the speed of the Crown Vic at impact?

While the misguided, believing that "time zero" must mean "impact," would look at the PCM data and conclude that the impact speed was 8mph (13km/h), the reality is that the impact speed most representative of the PCM data given what we know about the RDS is closer to about 18mph (29km/h). But, is there anything else we can find in the PCM data, aside from the RDS, which would support this analysis?

Because this is a crash test and, as previously noted, some aspects of the test were designed to result in certain data elements being illustrative of various points to be made as a function of this being a case problem, here, we can also

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look at the Key On Timer values. One should notice that the Key On Timer starts incrementing time showing 0.25s at buffer address EA000010 - as expected. We should recall that the ACM data parameter "Ignition Cycle Key On Timer at Start of Frontal Event (sec)" indicated that, in one second resolution, the frontal deployment and side trigger events occurred 16 seconds into the current ignition cycle.

In the PCM data, the Key On Timer reaches the 16second mark at address EA000500 and the 16sec period (given the resolution of this parameter and the different recording rate for the PCM data page frames) ends at about EA000550.

The page frame address we already concluded was most likely closest to impact or where module would have probably "felt" the collision was nearest the period when the PCM observed data page frame EA00530 - within the 1 second range bound by frames EA000500 and EA000550.

So where the ACM is keeping track of time from start up in one second intervals (resolution), the number of seconds it "saw" from startup to where we believe impact would clearly be a good fit with the data in the PCM. The Key On Timer, as described in the Data Limitations, is limited to 63.75sec. Most of the time, someone has been driving for more than 63.75 seconds before they're involved in a crash. The timer was used in the crash test as part of the foundation for a "proof" in this analysis. Here, the Crown Vic was running for a total of about 22 seconds from start up, through the crash, to shut down which explains the 63.75 values hanging at the "top" of the set of data from the previous ignition cycle.

Since the entire test took less than 25 seconds, the remaining approximately 3 seconds of "data" is from an unrelated cycle but it illustrates (1) where the PCM starts writing at power up (EA000010), (2) the Key On Timer function relative to the "Ignition Cycle Key On Timer at Start of Frontal Event (sec)" parameter in the ACM data and (3), since this was a test, helps us roughly identify where impact really would have been in the data set, knowing it's not at "time zero."

thus far addressed is the speed of the Malibu at impact with the Nissan. Given the CDR data for the Malibu, it would seem that it could be between approximately 2 and 7mph (3.2 and 11.3km/h). With the data for the Malibu; however, it's difficult to be more specific.

The analysis of the closing velocity for the Crown Vic leads us to believe that the impact speed (since the Malibu is stopped) is about 19.1 to 19.9mph (30.7 to 32 km/h).

That's reasonably consistent with the PCM data suggesting the impact took place after an observed speed for the Crown Vic of 24mph (39km/h) and where the observed speed would be a low of about 18mph (29km/h). It would then be reasonable then to conclude the impact speed/ closing speed range one might report for the Crown Vic was approximately 18 to 20mph (29 to 32 km/h).

One more thing...

Now that the questions posed in the case problem have been answered - with the data provided thus far - one has to wonder...is there something more to be taken from this case problem? Yes, there is.

The Malibu CDR report in the problem statement section was provided intentionally using an out-of-date version of the CDR Tool Software. With that in mind, as we go about properly using CDR Tool retrieved data, we are reminded (from page 1 of every CDR 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. ..."

We are given and have adopted that the Nissan was stopped at impact; speed is zero.

Looking back at the data provided with the problem statement, we find the System Status at Non- Deployment data table as seen in Figure 1 was translated and reported using CDR Software version 11.0. But, if we were to look at the same raw CDR file opened in the latest version of the CDR Tool Software (16.2.1 at the time of this writing), the System Status at Non-Deployment data would look like what we see in Figure 2.

We have concluded, using various methods and bits of information, that the Malibu was stopped and in contact with the Nissan when struck from behind by the Crown Vic; it's speed was zero at that point. What we haven't

In the Data Limitations text, for both versions, we find that there is a mention of "... the Time Between Events (being) displayed in seconds. If the time between the two events is greater than five seconds, "N/A" is displayed in place of the

3. Given the order of impact(s) you have decided on, what are the speeds of the various vehicles at impact(s) (again, assuming the Maxima is stopped as the events unfold)?

110 Collision Magazine - Volume 10 Issue 2

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System Status At Non-Deployment SIR Warning Lamp Status Driver's Belt Switch Circuit Status

OFF UNBUCKLED Air Bag Not Suppressed 26346 0.00

Passenger SIR Suppression Switch Circuit Status (if equipped) Ignition Cycles At Non-Deployment Maximum SDM Algorithm Longitudinal Velocity Change (MPH)

Figure 1: System Status translated and reported using CDR Software version 11.0 Vehicle Speed Seconds Before AE At Non-Deployment Engine Speed (RPM) System Status (MPH) SIR Warning Lamp Status 12 1536 -5 Driver's Belt Switch Circuit Status -4 7 1216 768 Passenger-3SIR Suppression Switch 2Circuit Status (if equipped) 768 -2 0 Ignition Cycles At Non-Deployment 768 -1 0 Maximum SDM Algorithm Longitudinal Velocity Change (MPH) Time Between this Event and the Previous Event (sec)

Percent Throttle

15 0 0 0 0

OFF UNBUCKLED Air Bag Not Suppressed 26346 0.00 3.4

Figure 2: System Status translated and reported using CDR Software version 16.2.1

Brake Switch Circuit State Vehicle Speed displayed in time. ..." but notice-8Before there AE is no data parameter our(RPM) evaluation of the data and sequence. This also gives Seconds Engine Speed Percent Throttle OFF (MPH) -7 OFF the report for the specifi c element "time between events." us some insight into the probable pre-crash speed for the -5 12 1536 15 ON -6 -4 7 1216 0 the Nissan which, in the crash test, was Malibu when it hit -5 ON 2 parameter "Time768 0 -3 While we find the specifi cally worded data -4 ON actually about 6mph (9.65km/h).

768 0 -2 0 Between Events" in-3 other GM data sets, that specifi c verON 768 0 -1 0 OFF biage isn't used in -2 this particular system.

We do; however, -1 OFF Seconds Before AE

find the parameter translated in the later version here which reads: "Time Between this Event and the Previous Event (sec)."

Brakefor Switch The translated value for that parameter the Malibu data Seconds Before AE Circuit State set in the case problem is 3.4sec apparently indicating that -8 OFF from the end of an-7event preceding the recorded Non-DeOFF -6 ployment to "algorithm enable" for thatONrecorded Non-De-5 ON ployment a period of -4 3.4sec had elapsed.

ON -3

OFF

-2 OFF Going back, historically, we find that in CDR version 11.0 -1 OFF (released 6/2013) there is the entry in the Data Limitations for this system indicating that "... the Time Between Events (being) displayed in seconds. If the time between the two events is greater than five seconds, "N/A" is displayed in place of the time. ..." but only see a time between events listed as the "Time between Non-Deployment and Deployment Events" in a System Status data table when there's a Deployment event recorded (in that software version).

""

... when we're using CDR Tool retrieved data properly, we need to work from the original CDR file... not a PDF, not a printed and scanned copy, but the original, raw CDR or CDRx file created by the CDR Tool ...

While there are some who would be quick to point out that the Malibu in this crash was some 15 years old at the time of the crash test, the reality is that it had only recently been taken off the road in an unrelated insurance claim (in However, in version 11.1 (released 9/2013) and for later 2015) and then found its way into the crash testing from versions, the parameter verbiage changed for a Deployment the NEAIFI Conference highlighted earlier in this issue of and was added for a Non-Deployment event to reflect the Collision. While one might brush this off as an "irrelevant legacy" vehicle, that ignorance belies the fact that these "Time Between this Event and the Previous Event (sec)." cars and the systems in them are still on the road and still In the context of this case problem, it should be clear that get in crashes...every day. But the final significant "lesson this information would have been useful earlier in the analy- learned" here is that when we're using CDR Tool retrieved sis. Had we known, for sure, that there was an event 3.4 data properly, we need to work from the original CDR file... seconds before the recorded Non-Deployment and we evalu- not a PDF, not a printed and scanned copy, but the original, ated the recorded event as we did, we could have concluded raw CDR or CDRx file created by the CDR Tool at the end 1G1ND52J5Y6****** Page 3 of 4 that the "previous event" was when the Malibu rearended of the imaging/download process and open it for review in the Nissan (which, by the way, is consistent with the timing the latest version of the CDR Tool software. of the crash test) and it would have further strengthened www.collisionpublishing.com

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C

2015 Vol.10 Iss.2

ollision C The International Compendium for Crash Research

Volume 10 Issue 2

FAll 2015

Collision: The International Compendium for Crash Research

Reviews of Crash Testing

Occupant Kinematics in Low-Speed Bumper-to-Bumper Rear Impacts

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Volume 10, Issue 2 - FALL 2015

Traffic Camera Video Analysis Digital Forensics in Accident Reconstruction

New format. More data. More ARC-CSI. The flash drive included with this issue of Collision is from the 2015 ARC-CSI Crash Conference. The flash drive contains over 7 GB of crash data including crash test videos, digital photos, CDR data, drawing files, and more.

Flas h Drive