Volume 7, Issue 1

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DV nsid IT t I M crip M ans SU Tr R ility CD issib 12 Adm 20 ock

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2012 Vol.7 Iss.1

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ollision C The International Compendium for Crash Research

Volume 7 Issue 1

"Your team is feeling the pressure of being called out to measure the scene of a crash on a busy section of major freeway.

Traffic is backing up and you still have at least another hour of data collection to do.

Once you are finally done and the road is cleared, you head back to upload the data collected to your computer and start diagramming when a moment of panic strikes. In a hurry to finish, a few key total station points were missed."

Avoid this situation with 3D laser scanning and Virtual Total Station What is Virtual Total Station?

From the comfort and safety of your desk, navigate within a 3D scan and 'shoot' measurement points you deem important. Create a printable measurement log showing X,Y,Z position of points, just as you would with a manual total station.

· Increase officer safety by taking measurements at your computer · No traverse when a point isn't in the line of sight, simply click on any point in the scan · At any time, capture a measurement that may have been overlooked To find out more about the benefits of 3D laser scanning and the Virtual Total Station contact Visual Statement today.

Volume 7, Issue 1 - Spring 2012

· Keep time in the field to a minimum by cutting data collection time

Collision: The International Compendium for Crash Research

THE VIRTUAL TOTAL STATION

Spring 2012

Becoming an Effective Expert Witness Mock Admissibility Hearing Using CDR Accuracy of Toyota EDRs Information Analysis for the Collision Analyst

Plus Articles from the

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Crash site evidenCe

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

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

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Contents Spring 2012

inside

Volume 7 Issue 1

4

Letter From the Editor

6

NAPARS: Letter From the President

8

Training Calendar

22

features 10

Accident Reconstruction of an Unwitnessed Bicycle Mishap

by Timothy Joganich 22

A Method for Creating Photograph Textured Planes and Camera Positions in HVE Simulations

50

by Brad R. Shults 38

Using the Monte Carlo Method with Crash Event Data

50

Accuracy of Toyota Event Data Recorders

66

Becoming an Effective Expert Witness

70

Transcript of the CDR Summit Mock Admissibility Hearing for Trial Preparation

by Roger W. Barrette

by Richard R. Ruth

by William J. Melkonian

by Ron Zacapa 76

Information Analysis for the Collision Analyst

84

Cdr Data From More Than One Car? Fitting It All Together

84

by Jerry S. Ogden, Mathew Martonovich, Zachariah Weimer, and Katrina M. Kloberdanz

by Raymond Wangler 92

Close-Range Photogrammetry for Sightline Obstruction Determination

by Lee DeChant and Gary Cooper 100

Toyota, Lexus, and Scion CDR Coverage

110

Using Data from a DriveCam Video Event Recorder to Reconstruct a Hard Braking Event

by Kent E. Boots

100

by Nathan A. Rose, William T.C. Neale and Neal R. Carter

opinion 121

Peer Review

by Raymond M. Brach, R. Matthew Brach, Jarrod Carter, Joseph Marsh, Donald Parker, Chris Van Ee, and Michael Varat

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Collision Magazine 3

Letter from the editor As we put the final touches on this issue of Collision, this is turning out to be one of our larger issues. Packed with informative articles, opinion pieces and more, this issue of Collision will surely be one that will stand out. It may be a good place or a bad place to be - depending on perspective, I suppose - but as we finalize this issue, we have new, fresh and interesting material to carry over to the next issue. For my part, I see that as an indication of Collision’s acceptance within the community as a function of our history of working to promote the widest distribution of information possible.

On the subject of innovation, with this issue, we’re happy to announce the “grand opening” of sort of two web destinations that will surely become as widely recognized as Collision. The Crash Forum (www.CrashForum.info) and the Accident Reconstruction Search Engine (www.ARSearchEngine. com) are two new cooperative efforts sponsored, in part, by Collision magazine that were months in the planning and development and have been launched since the last issue of Collision.

The Crash Forum (featured later in this issue) is truly the logical evolutionary step from older email lists and less viable chat-style forums to a dynamic and meaningful resource. Featuring the ability to attach files, photos and other documents directly to a post in the Forum, it will, over time, become the premier destination for easy-to-use topical searches for everything from general reconstruction topics to Crash Data Retrieval Tool technical support and analytical discussions.

On the subject of searches, another truly innovative tool sponsored in part by Collision is the Accident Reconstruction Search Engine web resource. Using a specially designed and focused, Google-based search engine, ARSearchEngine.com brings the power of a Google internet search and the focus of the Accident Reconstruction community in one powerful search engine. Selected keywords and resources are indexed directly to this new search engine allowing crash investigators and reconstructionists everywhere to perform relevant searches pertaining to the industry of accident reconstruction and traffic safety. Searches at ARSearchEngine.com are narrowly focused under a special arrangement with Google to bring you more focused, topical results. By design, the new search engine eliminates or minimizes unrelated content and focuses on crash related websites, images and the like to efficiently bring you more relevant content than a more broad internet search.

As this issue reaches mailboxes across the globe, we will have wrapped up the 11th annual ARC-CSI Crash Conference. We have compiled an incredible fleet of vehicles to crash and our line-up of speaker presentations will make for some really interesting articles for the next issue of Collision.

It’s all about innovation and moving forward. As Collision moves into its 7th year of publication, those are ideals we can all benefit from.

Managing Editor

4

Collision Magazine

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BACK ISSUES

Collision Publishing LLC PO Box 3383 Kirkland, WA 98083-3383 Toll Free: 800-280-7940 Email: editor@collisionpublishing.com Web: www.collisionpublishing.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, © 2006-12 Collision Publishing LLC. 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. Dissenting 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 email to the editor at editor@collisionpublishing.com or sending a “regular mail” letter to us at PO Box 3383, Kirkland, WA 98083-3383. By sending correspondence to either our email 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 email 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 reemerge, the existence of this publication and data disk in that universe cannot be guaranteed.

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

ADVERTISER INDEX

PAGE

4N6XPRT Systems Inside Back ARAS360 1 ARC Network 69 CDR User's Summit 90 CDR Training 108 Collision Publishing 109 Collision Safety Institute 9 Crash Data Group (CDR) 20,21 Crash Forum 36 CrashTest-Service.com 90 Engineering Dynamics Inside Front Eos Systems: Photomodeler 91 iWitness 99 Laser Technogies 49 MapScenes 2, 61-64 SAE 35 NAPARS 6 Northwestern University 75 Vericom Computers 65 Visual Statement 48, 74, Back Cover

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

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.collisionpublishing.com www.collisionpublishing.com

Included with this issue of Collision

is the 2012 CDR Summit data DVD. The CDR Summit DVD includes all of the speaker Power Point presentations, and any other supporting data compiled in a easy to review, menu-driven data DVD.

Next CDR Summit January 21-23, 2013 www.crashconferences.com Collision Magazine 5

Premier Industry Partner Letter from the President

Over the past few years we have seen many changes in the crash investigation and reconstruction field, and the coming year looks to be no different. Training, testing, hardware, software….everything is evolving very quickly.

For example, the federal requirements under Part 563 take effect in just a few months on September 1, 2012. That will ensure that almost every car and light truck sold in the U.S. is recording a host of crash data, with a commercially available tool for sale to access it. While only a few years ago it was a pleasant surprise to get crash data from one vehicle in a two vehicle crash…..soon it will be commonplace to get data from at least one or both vehicles.

And with prolific use of EDR data, I expect to see more states joining the current list of 13 with EDR laws that define (or restrict) access to or use of crash data. Make sure

to bookmark the EDR Law page at www.harristechnical. com to keep current with the changes.

NAPARS’s recent alliance with the University of Tulsa Crash Reconstruction Research Consortium allows our members access to the TU-CRRC crash test database and Members Only sections, which should be growing steadily. Crash testing throughout the year is added to the database, which includes photographs, video, and test results. Also available are a number of technical articles, such as “Crash Testing with a DDEC 10 Equipped Tractor” and “Interpreting the CAN data for a 2010 Toyota Camry”. Email NAPARS if you do not have the username and password.

NAPARS works diligently to maintain communications with its membership throughout the year, but modern SPAM filters work very hard to make that task difficult.

Newsletters and special email announcements are sent out regularly, but often can be relegated to a spam folder or be rejected all together due to an outdated email address.

Please make sure that NAPARS.org is allowed by your Internet filters and that you check your personal information on the NAPARS web site to ensure that we have your current email address.

Finally, 2012 is an election year for NAPARS. Anyone interested in serving on the Board of Directors in any capacity can participate in the general meeting in October at the Combined Conference or email their nominations to NAPARS once the election cycle is announced. I hope to see many of you at the 2012 ARC-CSI Conference. Stop by the NAPARS table and let us know how we are doing.

Chuck Veppert President, NAPARS

The National Association of Professional Accident Reconstruction Specialists is a non-profit organization whose members have joined together to share the challenge of dealing with the complex problems of accident reconstruction and to upgrade and ultimately professionalize the accident reconstruction field.

6

Collision Magazine

www.collisionpublishing.com

www.napars.org

Industry Partners IAARS is one of the oldest associations dedicated to

expanding the knowledge and training of accident reconstructionists. Formed in 1980, we have members from several different countries as well as many different states across the U.S. We have three levels of membership. Our highest level, Fellow, includes a peer review of a reconstruction project. This peer review feature can be very beneficial to a member seeking expert witness qualification in court. One of the criteria a judge can consider when deciding if a person qualifies as an expert is a peer review of his or her work. We offer opportunities for a member to publish work or research in the field of accident reconstruction. This, also, can be very important when qualifying as an expert witness in court proceedings.

Membership in IAARS has other advantages, including a dis-

ASPACI is a non-profit society whose members have

joined to share in the challenge of analysing the complicated issues of road collision investigation.

ASPACI was formed in 1991 by a group of police and collision reconstructionists who felt there was a need for information sharing between police and associated institutions in the Australia, South East Asia, New Zealand and South Pacific area.

Its primary function is to assist in the spreading of knowledge and enhancing expertise in the science. The Association seeks to promote a professional approach to collision investigation and to encourage the highest standard of ethics, integrity and honesty among its members.

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.

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.

count (from IAARS) on a subscription to Collision Magazine, member discounts at our training seminars and others listed on our web page, www.iaars.org.

We have annual training opportunities at different locations across the country. We try to have some sort of actual vehicle testing at each seminar, including full scale vehicle crashes. As a member, you would have access to a vast archive of results from many different and sometimes quite unique tests.

Sincerely, Dan Lofgren President, IAARS www.iaars.org The early days saw the number of members vary with the base of the association in Victoria, Australia. ASPACI became ASPACI Incorporated in 1993 in Victoria. Now we boast members from all over mainland Australia, New Zealand., United Kingdom, and the United States, as well as parts of Asia.

Membership to ASPACI is open to all persons engaged in collision investigation and its allied fields. The association members include Engineers, Police Officers, Independent Consultants, Scientists, Insurance Corporations, Manufacturers and Safety personnel. ASPACI is not a Trade Association or Political Lobby group, nor is it a Police organisation.

User's Summit and a free copy of Greg Russell's Excel Crush 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!

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 $79.00 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

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

For information on becoming a Collision Industry Partner send an email - editor@collisionpublishing.com www.collisionpublishing.com

Collision Magazine 7

training calendar for Accident Reconstruction and Traffic Investigation Collision Publishing (800) 280-7940 | www.crashconferences.com CDR Train the Trainer Course Houston, TX January 20, 2013

CDR Summit Houston, TX January 21-23, 2013

Collision Claims Expo Houston, TX January 24-25, 2013

Collision Safety Institute (858) 484-9794 | www.collisionpublishing.com CDR Data Analyst Course Des Moines, IA August 20-24, 2012

CDR Data Analyst Course Shawnee, KS September 17-21, 2012

CDR Data Analyst Course Glendale, AZ October 1-5, 2012

Engineering Dynamics Corp: (888) 768-6216 | www.edccorp.com EDC Reconstruction Course Coral Gables, FL November 5 - 9, 2012

EDC Simulations Course Burbank, CA January 14 - 18, 2013

2013 HVE Forum San Diego, CA February 25 - March 1, 2013

FactualDiagrams.com (888) 768-6216 | www.edccorp.com Basic/Intermediate Diagramming with CrashZone/CrimeZone Lakewood, CO August 13, 2012

Forensic Mapping System Course Citrus Heights, CA August 20, 2012

Advanced Diagramming with CrashZone/CrimeZone Sacramento, CA September 4, 2012

Northwestern University CPS (800) 323-4011 | nucps.northwestern.edu Traffic Crash Reconstruction 3 N. Las Vegas, NV August 27-31, 2012

Motorcycle Crash Reconstruction Evanston, IL September 10-14, 2012

Crash Investigation 1 Evanston, IL September 10-21, 2012

Crash Investigation 1 Urbana, IL September 17-28, 2012

Crash Investigation 2 Evanston, IL September 24- October 5, 2012

Vehicle Dynamics Evanston, IL October 8-12, 2012

Traffic Crash Reconstruction 1 Evanston, IL October 15-26, 2012

Traffic Crash Reconstruction 2 Evanston, IL October 29 - November 2, 2012

Traffic Crash Reconstruction 3 Evanston, IL February 25 - March 1, 2013

CDR Technician Level 1 Evanston, IL March 7, 2013

CDR Technician Level 2 Evanston, IL March 8, 2013

CDR Data Analyst Evanston, IL March 11-15, 2013

If you would like to list an upcoming course or event in Collision Magazine, please email editor@collisionpublishing.com

Accident Reconstruction of an Unwitnessed Bicycle Mishap

A

bstract

This paper presents a case study of the accident reconstruction for a bicycle mishap involving a 49-year-old female cyclist, who was found semi-conscious along the edge of the road, without any immediately apparent evidence of vehicle interaction. Based on this preliminary information, the investigating police authorities initially surmised that the cyclist experienced a sole-bicycle mishap, such as simply losing her balance, hitting a pot hole or rock, or accidently riding off the edge of the road.

A reconstruction employing standard accident reconstruction techniques, as well as those unique to bicycle accident reconstruction, found otherwise. In order to determine the likely accident scenario to a reasonable degree of engineering certainty, the physical findings, including the forensic evidence on the bicycle and on the cyclist’s clothing, the injury pattern, and roadway parameters were assessed using the fundamental laws of physics and principles of biomechanics.

The author concluded that the cyclist was contacted by a phantom vehicle, i.e. hit and run, as opposed to experiencing a sole bicycle mishap. The reconstruction also assessed the cyclist’s riding behavior which was found to be in accordance with good riding practices.

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Collision Magazine

www.collisionpublishing.com

Timothy Joganich, MS, CHFP Senior Engineer, ARCCA Inc.

I

ntroduction

Approximately 800 cyclists are killed and 500,000 are injured annually in the United States. (Pucher, 1999.) Pucher et al. states that “Most cycling accidents and injuries result from falls or collisions with fixed objects and thus are considered single-vehicle accidents or sole-bicycle mishaps. However, collisions with motor vehicles increase the risk of hospitalization nearly fourfold and account for well over 90% of cycling fatalities.”

Bicycle accident reconstructions are generally undertaken to determine how the bicycle accident occurred. The accident reconstruction process involves correlating the www.collisionpublishing.com

Collision Magazine 11

physical evidence with the accident dynamics, based on the fundamental laws and principles of physics, engineering, and biomechanics. (Hay, 1988; Nahum 1994; Resnick 1977; Eubanks 1999.) Specifically, observations or findings are collected and analyzed to establish theories.

These theories are then confirmed or rejected to arrive, if possible, at a conclusion that delineates the likely accident scenario. Similar to automotive accident reconstruction, where a driver’s behavior is considered, bicycle accident reconstruction can also assess a cyclist’s riding behavior in the context of nationally recognized good riding practices.

The motivation for undertaking such accident reconstruction depends, in part, on the severity of the injuries and potential liabilities.

The following case study entailed reconstructing a non-fatal bicycle mishap without any immediate evidence of vehicle interaction and without any eyewitness accounts. The bicycle did not exhibit any gross damage suggesting vehicle interaction, and the cyclist was found semi-conscious without any recollection of her incident. The one witness who did come upon the scene came sometime afterward.

Consequently, the reconstruction took on a greater importance due to the absence of any direct explanations of how the sole-bicycle mishap occurred.

C

Medical records indicate that the cyclist sustained a fracture of the C6 spinous process and numerous abrasions and lacerations to her head and face, as well as other parts of her body including: • 3 x 3 cm contusion over the left sternocleidomastoid muscle • 2 cm left chin laceration • Left forehead and cheek contusion • Left shoulder skin abrasion • Left mid-thigh skin abrasion • Right posterior elbow abrasion • Right dorsal (back) fingers abrasions In addition, photographs show that the cyclist sustained a contusion to her right lower buttock. (See Figure 2)

ase Facts

According to the local police incident report, on August 26, 2009, at or about 7:42 a.m., a 49-yearold female cyclist was involved in a bicycle mishap in New Britain Township, Pennsylvania. (See Figure 1) A witness, another cyclist who was riding in a southbound direction on Newville Road, stated that he found the cyclist semiconscious on Newville Road just south of Curly Mill Road lying in the road on her stomach with her bicycle behind her.

Figure 2: Contusion to right buttock

S Figure 1: Roads of Interest 12

Collision Magazine

tatements

According to the witness who found the cyclist, he initially thought the cyclist was riding northbound on Newville Road, presumably because she was found along the northbound edge of Newville Road. The cyclist herself stated that she did not recall any specifics of the crash. The last thing she remembered was making a right turn onto Newville Road heading southbound. The cyclist

www.collisionpublishing.com

further stated that she was on her usual 10 mile ride that would take her approximately 50 minutes and she would ride with the flow of traffic toward the center of the road on Newville Road, because of the nature of the road. She also stated that the weather was dry and sunny.

S

ite Inspection

Newville Road is a lightly traveled rural road with a posted speed limit of 35 miles-per-hour. It runs in an essentially straight line from the T-intersection at Curly Mill Road to New Galena Road and is approximately 0.6 miles long. (See Figure 3) The cyclist’s point of rest (POR) was identified using photographs taken shortly after the subject incident showing apparent blood stains on the road. The POR is identified in Figure 3 by the cone on the northbound side of Newville Road, and was approximately 393 feet south of Curley Mill Road. The roadway at this point measured 13 feet 10 inches in width. Soft shoulders of varying widths were, located on both sides in the immediate area. The shoulders widths at the POR area were 2 feet and 2 feet 8 inches wide for the southbound

and northbound shoulders, respectively. Drainage ditches were immediately adjacent to each shoulder. Newville Road slopes downward an average of 3.6 percent from Curly Mill Road to the cyclist’s POR. The roadway in the POR area exhibits a crown measuring 4 to 6 degrees on the southbound side. There is loose gravel on both shoulders.

B

icycle/Clothing Inspection

The subject bicycle was a 51 centimeter Specialized Vita hybrid bicycle (S/N 8KI90135). (See Figure 4) While the subject bicycle was absent any major damage, various components of the bicycle exhibited minor damage. The right side of the bicycle seat was deformed upward about the longitudinal axis approximately 10 degrees counterclockwise (viewed from rear to front). The seat also had scuff marks and relatively small tears on the right rear. (See Figure 5) The bottom and top edge of the seat measured 35 and 36 ½ inches vertically from the ground, respectively.

The latter dimension reflects the height of the bottom of the cyclist’s buttock off the ground. The right pedal and rear derailleur exhibited scrape marks. The front and rear

Figure 3: Southbound view of Newville Road www.collisionpublishing.com

Collision Magazine 13

tires were in good condition and appeared to be relatively new. Both tires exhibited scuff marks along the center of the tread. The subject bicycle was equipped with v-brakes that were in good operational condition. According to an interview with the cyclist, the bicycle was 6 months old at the time of the subject incident and had not been involved in any previous mishaps.

The cyclist was wearing a hard shell Bell Solar bicycle helmet (S/N C3555434; June 2008) at the time of her subject incident. The subject helmet displayed labeling indicating

that it complied with CPSC (Consumer Product Safety Commission) safety standards. (16 CFR1203.) Significant damage to both the hard shell and polystyrene liner was evident on the left side along the lower edge extending from the front. (See Figure 6) The hard shell had vertically oriented abrasions, while both the hard shell and the polystyrene liner were cracked from the lower edge to the top. There were apparent blood stains on the left side chin straps and on the interior surfaces of the polystyrene liner.

Figure 4: Subject bicycle: Specialized Vita

Figure 5a: Subject seat deformation

14

Collision Magazine

Figure 5b: Forensic markings on right rear

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6:23 a.m. with civil twilight at 5:54 a.m. These climatology and astronomical data are consistent with the cyclist’s statement that the weather was dry and sunny.

S

ummary of Findings:

1. The 49-year-old female cyclist was involved in a bicycle mishap at or about 7:25 a.m. on August 26, 2009, on Newville Road between Curly Mill Road and New Galena Road in Chalfont, Pennsylvania.

2. The cyclist came to rest on Newville Road approximately 393 feet south of Curly Mill Road.

3. At the time, the cyclist was riding a Specialized Vita hybrid bicycle that was in good operating condition and was wearing her CPSC approved bicycle helmet.

Figure 6: Subject helmet

4. As a result of the cyclist’s subject incident, she sustained a C6 spinous process fracture, left facial and left upper body abrasions/lacerations, right arm/hand abrasions, and a contusion to the right lower buttock.

The cyclist was also wearing running shoes, black Lycra cycling shorts, and a Nike tank-top shirt at the time of her subject incident. The shorts and shirts were cut along the sides consistent with removal for medical treatment.

(Eubanks, 1998.) While these articles of clothing were absent any major damage such as ripping and road abrasions, the cycling shorts showed evidence of scuffing and brush marks in the right buttock area. This evidence was particularity noticeable along the upper seam for the chamois insert. (See Figure 7) The Lycra material in this area also exhibited bunching.

5. Forensic evidence was observed on the seat and cycling shorts. There were scuff and tears on the rear of the seat and the seat was deformed about the longitudinal axis in a counterclockwise direction. The right buttock area of the cycling shorts exhibited scuff and brush marks.

6. The subject bicycle helmet exhibited damage to the left side area extending from the front.

7. Weather was clear and dry. The cyclist was riding during daylight hours.

R

econstruction

Figure 7: Forensic Evidence on Cycling Shorts

C

limatology/Astronomical

The temperature on the date and time of the subject incident was between 68 and 73 degrees Fahrenheit. Sky conditions were clear with no precipitation. On the date of the subject incident, sunrise was at

Based on the cyclist’s injury pattern, helmet damage and witness statement, the cyclist’s kinematics entailed translating forward from her upright riding posture and impacting the ground in a prone head-leading orientation, predominately on her left side. A number of possible accident scenarios consistent with these kinematics are considered for this reconstruction analysis. There are two typical causes of a cyclist translating forward over the handle bar during sole-bicycle mishaps: 1) the bicycle being suddenly decelerated by the bicycle’s front wheel interacting with an obstacle i.e. pot hole, grate or curb, or 2) excessive front wheel braking. There was no evidence in this case to support the former scenario and it was consequently ruled out.

The second scenario of excessive braking is considered later in this paper. Beyond these two classical accident scenarios involving the bicycle being suddenly decelerated, it is also reasonable to consider scenarios with the cyclist losing con-

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Collision Magazine 15

trol, because they also would likely result in forward leading kinematics, given the cyclist’s forward velocity. Given the absence of any major damage to the bicycle, such as from interaction with the front part of a vehicle, the forensic evidence on the cyclist’s shorts and bicycle seat, and the contusion on the cyclist’s right lower buttock, it is reasonable to consider a scenario of the cyclist losing control from being impacted on her right buttock. Furthermore, it is reasonable to consider that the impact was from a protruding car mirror of a phantom vehicle; this scenario is the focus of this reconstruction.

The key forensic evidence in this case collectively constructs a cogent argument supporting a phantom vehicle scenario. Initially, the scuff marks on the cyclist’s shorts and the cyclist’s injury pattern to her right buttock are consistent with a blunt object/sliding type of impact. Such forensic evidence is often seen in pedestrian vehicle impacts where the pedestrian impacts a blunt object and then slides over the vehicle’s relatively smooth exterior surface.

(Eubanks, 1998; Becker 2003.) The scuffing marks on the cycling shorts are also consistent with the author’s personal experience of scuffing marks occurring on the portions of cycling shorts that experience repetitive sliding against the relatively smooth surface of the seat.

Additionally, the scuff marks along the seam of the chamois insert demonstrate a relatively close proximal relationship to the area of the bruising pattern on the cyclist’s right buttock. Furthermore, the contact forces between the cyclist’s buttock/seat and car mirror seems plausible for causing bruising. A random survey of seven vehicles with collapsible side mirrors (2006 BMW 335i, 2007 Kia Spectra, 2009 Honda Civic, 2010 Mercedes-Benz C300, 2003 Subaru Outback, 2008 Volkswagen, GTI, 2008 Volvo V70) at the investigator’s office parking lot dem-

Figure 8a: Photographic overlay of driver mirror

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onstrated that the average force required for folding in the collapsible mirrors was 17.8 pounds and ranged from 10 to 26 pounds. These forces most likely reflect a lower bound since the forces were measured at a perpendicular orientation to the mirror whereas a contact with the cyclist’s buttock and bicycle seat would result in an angled impact. One must also keep in mind that the forces would be shared between the cyclist’s buttock and seat and not all mirrors are collapsible. It also seems reasonable that these force levels could cause the cyclist to lose control.

Perhaps most compelling is the relatively close proximity of the cyclist’s injury pattern to her right buttock, scuff marks on the cyclist’s shorts, and scuff marks on the right rear seat to a driver’s side mirror. The same random survey (with the inclusion of a 2002 Ford Thunderbird with a non-collapsible mirror) demonstrated that a driver’s mirror was within the boundaries of the cyclist’s right buttock and seat. The bottom of the car mirrors ranged from 35 to 39 inches while the bottom of the cyclist’s buttock was 36 ½ inches vertically off the ground. The close proximity of the aforementioned evidence was demonstrated with a photographic overlay using the victim and a driver’s mirror/ vehicle. (See Figure 8 a and 8b) [author’s note: the photographic overlay is not meant to imply an exact impact area or exact type of mirror/vehicle.] Figure 8b highlights the seam of the chamois insert (zig-zag), damage to the seat (gray area) and outlines the car mirror. The scuff marks on the cyclist’s shorts would be within the boundaries of the car mirror as depicted. This exercise also demonstrated that it is plausible that there would be adequate lateral clearance between the bicycle and a phantom vehicle.

It is also reasonable to consider that the cyclist’s extensive injury pattern is consistent with a phantom vehicle scenario, as opposed to a relatively low speed sole-bicycle mishap

Figure 8b: Proximity of seat scuff marks, cycling short scuff marks, and car mirror

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due to a loss of control, such as from any gravel that may have been present. According to the cyclist’s witness statement, her average speed would have been on the order of 12 miles-per-hour. Based on this author’s personal cycling experience and experience of investigating numerous bicycle mishaps, sole-bicycle mishaps such as from a loss of control from loose gravel typically result in relatively minor injuries in comparison to high energy bicycle interactions with vehicles. While this point in isolation does not solely support the notion of a phantom vehicle scenario, it is consistent with the evidence as a whole suggesting a phantom vehicle scenario.

Ruling out alternative explanations for the forensic evidence further substantiates a phantom vehicle scenario.

First is the damage to the seat. The damage to the seat consists of scuff marks along the right back side, tearing along the right bottom corner edge, and an upward deformation on the right side. One could initially speculate that this deformation resulted from contact forces between a seat and the ground during a loss of control sole-bicycle mishap where the cyclist and the bicycle fell over to the right.

This deformation, however, would be practically impossible because the force vector would be insufficient to cause the deformation evident in the subject seat. Any contact between the seat and ground in the aforementioned scenario would result in a relatively low magnitude of force.

The author has examined hundreds of bicycles that have either been thrown or have fallen to the ground with no observable deformation. Additionally, the resulting force vector would be oriented perpendicular to the main bicycle frame, thereby minimizing the tendency to upwardly deform a seat. In contrast, it is relatively easy to envision the aforementioned seat deformation occurring from a relatively high-energy contact with a car mirror of an appropriate height and subsequent interaction with the mirror and/or vehicle. Furthermore, the scuff marks on the seat are consistent with a blunt object/sliding type of impact with a mirror in a similar manner as evident by the scuff marks on the subject cycling shorts. Also, if the right side of the seat impacted the asphalt, it would be reasonable to expect abrasions and tearing on this surface as opposed to just the right bottom corner.

Secondly, a phantom vehicle scenario is consistent with being able to rule out the alternative explanation for the cyclist’s right buttock injury. In the absence of contact with a mirror, the only reasonable explanation for a buttock injury would necessitate the cyclist’s buttock impacting the ground. Any such impact would likely include sliding along the pavement resulting in abrasions commonly referred to as “road rash,” as opposed to just a contusion.

Furthermore, the cyclist’s shorts would have also exhibited

significantly more damage in the form of shredding as the shorts interacted with the relatively rough pavement surface. This type of damage to cycling shorts is commonly seen in crashes during bicycle racing. (See Figure 9)

Figure 9: Skin abrasions and shredded cycling shorts from sliding

The reconstruction analysis also considered the dimensional aspects of the roadway. The phantom vehicle scenario obviously necessitates adequate room for a vehicle to pass the cyclist on the right. Assuming for purposes of discussion that the vehicle was a medium size 2009 4-door Ford Taurus which has an overall width of 6 feet 3 inches (tire track 64 inches) and the cyclist was in the same lane of travel as the vehicle. These parameters indicate that the cyclist was positioned anywhere from the center of the lane to approximately 3 ½ feet right of center (distance between the center of the roadway and the drainage ditch is 9 feet, 7 feet roadway and 2 feet shoulder). This lane position is reasonable based on the riding conditions and the roadway’s topography and is discussed below. While there was no evidence to definitively determine the position of the cyclist on the roadway, this analysis demonstrates the plausibility of the phantom vehicle scenario.

A scenario in which a phantom vehicle passes on the right also reconciles the apparent discrepancy between the cyclist’s POR with her stated direction of travel. Based on the cyclist’s POR on the northbound side of Newville Road, it would be reasonable to initially conclude, as the witness did, that the cyclist was traveling northbound on Newville Road. However, a vehicle mirror contacting the cyclist on her right buttock would have a tendency to direct the cyclist to her left into the northbound lane of Newville road, especially considering that the cyclist was likely near the center of the roadway.

As previously mentioned, the phantom vehicle scenario necessitates that the cyclist was riding near the center of Newville Road. Newville Road is in a rural setting with

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light traffic and unobstructed visibility. The center of the road was relatively smooth as compared to sides of the road which had broken and bumpy pavement. Given these riding conditions and roadway topography, the cyclist’s lane position near or at the center is consistent with nationally recognized good riding practices and with Pennsylvania statutes and guidelines for bicycle riding. Concepts of good riding indicate that, when appropriate, a cyclist should ride in the center of the lane or just to the right of the center. (League of American Bicyclists) Similarly, Title 75 of the Pennsylvania Consolidated Statutes for the operation of bicycles states that “a pedacycle operated at slower than prevailing speed shall be operated in accordance with he provision of Section 3301(b), unless it is unsafe to do so.” In essence, Section 3301 (b) mandates that the cyclist ride as close to the right as practicable not as possible.

Riding on the shoulder on rural road can provide the cyclist with a false sense of safety. The cyclist must still be alert to the special challenges that exist on shoulders. …When you do ride on the shoulder remember to consider the need to scan ahead for surface hazards, which are common on rural roads, less wellmaintained roads. Shoulders often deteriorate quickly due to reduced construction standards for shoulder as opposed to the actual road surface.

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ummary/Conclusions

In summary, the reconstruction analysis entailed assessing the consistency of the physical evidence with the fundamental laws of physics. The injury pattern and damage to the cyclist’s helmet indicate that the cyclist’s kinematics reflected a head leading forward trajectory. Other forensic evidence including the contusion to the cyclist’s right buttock, marks on the cycling shorts in the right buttock area, marks on the right rear of the bicycle seat, and deformation damage to the bicycle seat indicate that the cyclist was contacted in the right buttock by a blunt object resulting in the cyclist being projected forward and/or losing control of her bicycle. The right buttock impact area correlated with mirror heights of common sedan vehicles. Based on these findings and the reconstruction analysis, it was concluded that more than likely the driver’s mirror of a phantom vehicle passed to the right of the cyclist and contacted the cyclist on the right buttock, causing the cyclist to be accelerated forward and/or lose control of her bicycle. While excessive braking would cause the cyclist to exhibit similar forward head leading kinematics and is technically a possible scenario, this scenario was considered unlikely in light of the overwhelming evidence indicating the cyclist was impacted on the right buttock of a passing phantom vehicle, especially the right buttock contusion, and then forensic evidence on the cyclist’s shorts and the right rear of the bicycle seat.

Additionally, given the riding conditions and roadway topography, the cyclist’s riding position at or near the center of the roadway under the circumstance was reasonable and consistent with the Pennsylvania Statutes and guidelines and nationally recognized good cycling practices.

“" The concept of practicable is elaborated in the Road Riding section of the PennDot’s Bike Safe web site which states that a cyclist should ride in the usable width of the

The concept of practicable is elaborated in the Road Riding section of the PennDot’s Bike Safe web site which states that a cyclist should ride in the usable width of the road which“begins where you can ride without increased danger of falls, jolts or blowouts.” road which“begins where you can ride without increased danger of falls, jolts or blowouts.” (PennDot, 2010) Literature from the League of American Bicyclists discusses the hazards associated with roadway shoulders and edges. (League of American Bicyclists, 2005.) Specifically the instructor manual for the bicycle education program states:

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eferences

Pucher, J. et al. (1999) Bicycling renaissance in North America? Recent trends and alternative policies to promote cycling. Transportation Research Part A, 33(7/8), 625-654.

Hay, J.G. and Reid, J.G. (1988). Anatomy, mechanics and human motion. 2nd edition. Prentice-Hall, Inc. Englewood Cliffs, NJ. Nahum, A.M. and Gomez, M.A. (1994). Injury reconstruction: biomechanical analysis of accidental injury. Society of Automotive Engineers, Warrendale, PA. SAE Paper No. 940568.

Resnick, R. and Halliday, D. (1977). Physics. John Wiley & Sons, New York.

Eubanks, J. J. and Hill, P. F. (1999). Pedestrian Accident Reconstruction and Litigation. Lawyers & Judges Publishing Co., Inc., Tucson, AZ. 16 C.F.R. (Code of Federal Register) 1203- Safety Standard for Bicycle Helmets.

Eubanks, J.J. & Hill, P.F. (1998) Pedestrian Accident Reconstruction and Litigation. Layers & Judges Publishing Co., Inc., Tucson, AZ. Becker, T.L. (2003). Vehicle- Pedestrian- Bicycle Collision Investigation Manual 2nd Edition. Institute of Police Technology Management, University of North Florida.

Smart Cycling – Traffic Skills 101. League of American Bicyclists. Washington, DC. PennDot’s Bike Safe web stie: http://www.dot.state.pa.us/ BIKE/WEB/road_where.thm. Accessed March 16, 2010 BikeEd Program, League cycling Instructor Manual.

League of American Bicyclists, 2005

A

cknowledgments

The author would like to acknowledge Lori Renner for her technical editing assistance in preparing this paper and A’Liah Hines for her assistance in data collection.

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f interest is a recent automobile-bicycle incident that was reported on by ABC News on April 30, 2012. (http://abcnews.go.com/blogs/headlines/2012/04/tinybicycle-camera-helps-solve-hit-and-run/). The interaction between the automobile and cyclist was captured by a mini-camera on the bicycle of another cyclist who was riding behind the lead cyclist. The video shows an automobile coming up from behind and then interacting with the left side of the lead cyclist. The video images clearly show an interaction of the automobile’s right side mirror with the cyclist in the area of their buttock/seat. (See Figure 10) This incident documented on video further supports the possibility of phantom vehicle scenario in the subject investigation.

Figure 10: Selected video frame images

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Collision Magazine 19

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A Method for Creating Photograph Textured Planes and Camera Positions in HVE Simulations

Brad R. Shults Ponderosa Associates 22

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bstract

A method for creating photograph backgrounds in HVE is presented. This method is useful for comparing simulation results to accident photographs evidencing the scene. Through the use of survey equipment, photogrammetry software, and CAD software, a user may calculate the photograph plane and respective camera position in three-dimensional space of a particular photograph. A process

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for determining the three-dimensional camera position and photograph plane center is presented. A process for creating the photograph texture for HVE is presented. After presenting the above processes, an example crash from the 2011 ARC-CSI Crash Conference is presented.

Additionally, a case study photograph background is presented. Finally, results are discussed and recommendations for future work are presented.

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I

ntroduction

The purpose of presenting this procedure is to define a relatively quick and effective method for creating a realistic scene without going to the extent of creating time intensive scene surfaces, textures, and rendering outputs that are often created with more robust animation programs from HVE simulation outputs. From survey measurements, or in some cases 2D traces from an aerial, an HVE user can solve for properties important to accurately placing a photograph background plane and viewing camera. This allows the user to compare evidence predicted in an HVE simulation (whether it be a EDSMAC4, SIMON, or another HVE simulation program) against the evidence observed in an accident photograph. In addition, this method allows the user to create an accident specific background. This allows involved parties to reference familiar landmarks and more easily describe the accident.

The method presented in this paper may be applied to an accident site photograph, scene inspection photograph, or even a street view image from publicly available databases.

“

From survey measurements, or in some cases 2D traces from an aerial, an HVE user can solve for properties important to accurately placing a photograph background plane and viewing camera.

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P

rocedure

Before describing the specific procedure developed for placing a photograph background plane and camera position into an HVE scene, it is important to realize that other software packages and methods can be used to meet the same end. The procedure developed in this paper is the most streamlined method this author has been able to develop with survey equipment, PhotoModeler, 3D Studio Max, AutoCAD, and HVE. If a reader does not have all of these programs at their disposal, other programs or methods can be substituted to reach the same end. At a bare minimum, a user could implement this method with a camera, measuring tape, and HVE. The general procedure steps are as follows: 1. Collect Measurements: Measure the accident scene.

2. Photogrammetry: Determine the camera location and photograph plane coordinates via photogrammetry.

3. Export to CAD: Export the photograph plane to a CAD program containing the scene data planned for HVE simulations.

4. Scale Photo Plane: Scale the photograph plane from the camera location such that the photograph plane extends beyond all scene data. This will make for cleaner simulation images during video creations.

5. Create a Square Aspect Ratio: Create a square plane around the extents of the photograph plane. This helps to maintain the photograph detail in an HVE texture.

6. Save as a JPEG: Create a square plane containing the centered photograph as a JPEG. This JPEG should be saved to the user’s HVE > supportFiles > images > environments > EnvTextures – folder.

7. Texturing in HVE: Texture the square plane with the saved JPEG. Sometimes the JPEG will require mirroring and/or rotation due to sign conventions within a specific scene.

8. Enter Camera Setup Properties: Within a simulation event, create a “camera setup” view with the “Look At” properties being the photo plane center created when the photograph was scaled over the extents of scene data. Enter the “View from” properties from the photogrammetry work performed to solve for the camera position.

The previous procedure step explanations were over simplified to aid the general understanding of the process. Once

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Figure 1 – Photogrammetry Control Points

the general process is understood, it is anticipated that the reader can use other programs and methods to meet the same end of placing a photograph plane and camera position into an HVE simulation study. A more detailed explanation of these steps will now be presented.

Step 1 – Collect Measurements The accident scene needs to be measured and have several recognizable reference points available in the same photograph that a user wishes to create as a background for HVE simulations. For flat scenes with a high clarity aerial, it is often possible to solve for a camera’s positions from an aerial trace of features on the roadway. Skip lines, road edges, joint lines, building bases, and other examples will often allow a user to roughly determine a camera position. Higher accuracy in calculating the camera position is obtained when using three-dimensional survey data, especially when the scene is contoured and elevation changes are significant.

Figure 1 depicts an example accident photograph. This photograph is a staged collision from the 2011 ARC-CSI Crash Conference in Las Vegas, Nevada. Select survey

points are green dots circled in red. These points are a good example of adequate spread. In general, when using photogrammetry to determine a camera position and photograph plane, the more angular separation between points (e.g. spread on a single photograph image) the better the accuracy in triangulating the camera position. At the end of the process this creates a better alignment with the photograph background to the HVE simulation scene.

The circled reference points are used by a photogrammetry software package to determine the three-dimensional location and focal length of the camera used to take the photograph. It is beyond the scope of this paper to present the details and methodology of photogrammetry; however, the basic relation between survey measurements, photogrammetry, and HVE photograph properties will be described to a level such that one familiar with photogrammetry methods, CAD, and HVE will be able to recreate and understand the process of defining the camera’s location and the photo plane’s center. Ultimately, this process allows the user to enter calculated three-dimensional coordinates in HVE for a camera’s “View from” and “Look at” input cells.

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Collision Magazine 25

Step 2 - Photogrammetry

Step 4 – Scale Photo Plane

Now that the X, Y, and Z distance relations between several points in the photograph are known, photogrammetry may be performed to determine the camera and photograph plane relationship.

Once the photo planes have been imported into AutoCAD, the next step is to scale the photographs beyond the extent of scene geometry data. To do this the user needs to place a point for the solved camera location. The threedimensional camera points are available from the results of photogrammetry. The camera point will be used as the

Figure 2 depicts the PhotoModeler photogrammetry results for the cameras and photograph planes. Survey data is magenta. The camera positions are represented by blue camera symbols (containing X, Y, Z coordinates). Two cameras and photograph planes were solved for in this example. The resulting simulation views are presented in greater detail in the Results section.

Step 3 – Export to CAD The camera and photo plane positions have been defined via photogrammetry. The results should now be exported into a CAD program so the photograph planes can be prepared for HVE representation. PhotoModeler’s export option “Max Script .ms” allows this information to be exported such that the photograph planes can be prepared in CAD for representation in HVE. Figure 3 illustrates the results of opening the .ms export in 3D Studio Max. After opening the exported .ms file in 3D Studio Max, the next step is to export the scene to a CAD program such as AutoCAD. Exporting from 3DS as an AutoCAD file (DWG or DXF) is this author’s preferred method. Again, different users will have different preferences to reach the same end. The results of the export are presented in Figure 4 (the photograph planes are highlighted by blue coloring while the magenta colored data represents the survey data from the crash testing).

Figure 2: Photogrammetry Results 26

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Figure 3: Max Script Results in 3DS

Figure 4: Photo Planes in AutoCAD www.collisionpublishing.com

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“"

base scale point for the respective photograph plane. This scaling process will improve the appearance of the HVE simulation work and will allow the user to compare the photograph perspective matchup with the scene geometry data. Figure 5 depicts the scaling process.

This scaling process will improve the appearance of the HVE simulation

Note: The top figure is before scaling and the bottom figure is after scaling. Scaling is performed at the blue camera points.

Step 5 – Create a Square Aspect Ratio Next, the photograph planes need to have an overall square plane surrounding the extents of the photograph area. This is helpful for maintaining the aspect ratio and perspective of the photograph when HVE texturing is applied to the plane. It is helpful to keep the rectangular photograph plane outline as a sizing guide for texturing work within HVE. It is also helpful to have a square plane larger and over extending the longest aspect ratio of the photograph plane. This allows users to check that all of the photograph imagery has been textured into HVE and assure that nothing has been “cropped out” during the texturing process.

Figure 6 illustrates the results of adding a square texture plane to the center of the photograph plane. The square planes are light blue in color.

Step 6 – Save as a JPEG Now square JPEGs need to be created for the HVE textures. A simple method is plotting square images with AutoCAD’s plot to JPEG feature. Figure 7 depicts the square regions that are plotted (5,000 by 5,000 pixels is a good JPEG plot size) and saved to JPEGs for future textures.

Figure 5: Scaling Photo Planes in AutoCAD Beyond the Extent of Survey Data

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Figure 6: Creating Square Planes in AutoCAD

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Figure 7: AutoCAD JPEG Plotting Setup

These JPEGs should be saved to the user’s HVE > supportFiles > images > environments > EnvTextures – folder.

Step 7 – Texturing in HVE The scene can now be imported into HVE and the photograph planes textured with the JPEG images. It is helpful to have each photograph plane a unique color when importing from a CAD program. HVE separates objects by color in the scene’s geometry during import. This will allow a user to individually select, control, and manipulate each photograph plane independently from one another.

Figure 8: JPEGs Applied as HVE Textures in Simulation Scene

The process for applying the textures is the same as applying any texture in HVE. Simply use the 3-D Edit > Launch 3-D Editor and then adjust material colors and material textures. Often times it is best to change the photograph plane colors from their original color to all white with minimal shine/gloss. Photograph planes can also be made transparent to improve camera views when creating a video from the simulations. The results of texturing the photograph planes are presented in Figure 8.

Step 8 – Enter Camera Setup Properties The last step is to create cameras with properties that match the results of previous photogrammetry and CAD workup.

The photogrammetry work should provide the user with the camera location. Recall that HVE uses SAE sign convention standards. Depending on the software used for buildup work, this will often require the user to flip the sign results for y and z coordinates about the x-axis. The camera setup should view from the solved for photogrammetry camera coordinates and look at the center of the photograph plane after it was scaled beyond the survey data in the CAD work. From this point on, regular simulation work may be performed and the results compared against a photograph background may be examined. In addition, video output can be created and more realism is achieved.

Various transparency settings allow a user to highlight various evidence and surfaces. Transparency settings also will allow a user to remove photograph planes from view so that traditional simulation work may be carried out. This is especially useful if a particular scene and simulation have

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several views a user wishes to analyze or present. Figures 9 and 10 illustrate the ARC-CSI Crash vehicles approaching their areas of rest during HVE simulation studies.

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esults: Case Study Examples

The above procedure will now be applied for two examples. The first example will be familiar as it is the same 2011 ARC-CSI Crash Conference staged collision that was used to present the overall procedure of applying photograph backgrounds. The second example will be an altered case study example that was investigated by Ponderosa Associates.

The first example involves a 1997 Ford Aspire and 2011 Buick Lacrosse. This example is intended to highlight the capability of comparing a simulation predicted area of rest to the area of rest evidenced in an accident site photograph. Note that further work could be implemented to match simulation tire mark prediction to the tire marks witnessed in the accident site photograph. This would involve fine tuning many parameters such as road friction, tire friction settings, braking, tire damage, suspension, and other components. In this special case where we have complete accident scene video that can be directly compared to simulation video output, even more work could be done to closely replicate yaw rotation rates, crush deformation rates, and overall decelerations. The scope of this example is to simply highlight the utility of using photograph backgrounds within HVE to quickly compare the simulation results against the observed evidence. Appendix Figures 11 through 13 depict the results of this comparison.

The second example is a case that Ponderosa Associates has worked on. The collision and involved vehicles have been altered from the actual case; however, the background imagery, scene survey data, scene surfaces, and camera positions match actual casework results. The purpose of this

example is to demonstrate the utility of adding a scene inspection photograph to create a photo-realistic background. Note that in this example no comparisons are made to areas of rest as this was a case where no quality accident site photographs were taken or provided. The utility of creating this background is to aid witnesses and parties involved in describing the accident. Landmark references such as dirt road turnouts, fences, buildings, and culverts are often referred to by involved parties. Also, critical issues such as available sightline and certain perspectives at a given location can be better expressed and judged by viewers. Adding a scene photo background can aid descriptions and improve the effectiveness of a simulation in conveying key concepts. Appendix Figures 14 through 18 depict the visual results of this hypothetical case.

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onclusions

By acquiring scene measurements, performing photogrammetry, and by using various software programs it has been shown that evidence photographs can be accurately incorporated into HVE simulation studies. These photographs aid a reconstructionist in matching critical evidence and presenting available sightlines. In addition, these photographs bring a real world aspect to the simulations in a relatively quick turnaround. Once a user has gained experience with the presented process, a simulation scene with a photograph background can often be created in one day. Although various animation programs might be more robust in offering various lighting conditions, driver perspectives, witness perspectives, and camera rigs, the procedure presented within this paper is a quick and simple way to achieve similar results without spending the time or resource required to render more complex animations from HVE simulation outputs.

Figure 9: HVE Simulation Study View #1

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Figure 10: HVE Simulation Study View #2

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The procedure presented in this paper does have shortcomings. In general, HVE textures seem to have increased perspective accuracy when the photograph is less tilted relative to the scene. Perspective matching has known issues from photogrammetry software work to exporting to various CAD programs. PhotoModeler has developed tools to minimize perspective matching errors. It is beyond the scope of this paper to fully address the software issues involved with perspective matching accuracy, yet it is important to be aware of the issue.

Finally, the method of applying a photograph background in an HVE simulation increases the utility of HVE. Unique applications exist that this author has been successful in applying the above outlined method. Such applications include cases involving bicycles, snow mobiles, tubing hills, ski slopes, and others. By applying this method with some creative CAD work, a user will find many perception-reaction cases may be examined in greater detail within HVE than they may have originally thought possible.

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ecommendations For Future Work

As was briefly discussed earlier in this paper, two concepts should be further investigated.The first

concept is the relation between HVE texturing and the photograph plane’s three-dimensional angular position. It is suspected that perspective matching error increases as the photograph plane tilt increases (i.e. when the photograph plane becomes less orthogonal to the scene’s horizon). Roll and yaw angles of the photograph plane also seem to influence the perspective matching quality. It is suspected that the processing technique that HVE software uses for texturing, which requires square imaging to maintain clarity, is the main issue for losing clarity of a photograph’s perspective match to scene data as the photograph plane becomes less orthogonal. Deriving a method of quantifying the reduced perspective matching to the relation of the photograph’s plane angle would be an interesting and valuable study.

Secondly, if possible, the overall procedure should be streamlined to include less legwork, file management, and required software programs. Ideally, a user would be able to perform photogrammetry and enter parameters into a sub program that automatically textures and surfaces photograph planes according to the controlling variables.

Such variables include photogrammetry results, extent of scene data, and aspect ratios of evidence photographs.

Figure 11: 2011 ARC-CSI Crash Test and HVE Simulation Comparisons at Impact

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Figure 12: 2011 ARC-CSI Crash Test and HVE Simulation Comparisons at Midpoint Post Impact Travel

Figure 13: 2011 ARC-CSI Crash Test and HVE Simulation Comparisons at Areas of Rest 32

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Figure 14: Hypothetical Case Example, Isometric Environment View

Figure 15: Hypothetical Case Example, View from Photograph 1 with Solid Environment Surfaces www.collisionpublishing.com

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Figure 16: Hypothetical Case Example, View from Photograph 2 with Solid Environment Surfaces

Figure 17: Hypothetical Case Example, View from Photograph 1 with Transparent Environment Surfaces

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Figure 18: Hypothetical Case Example, View from Photograph 2 with Transparent Environment Surfaces

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Collision Magazine 35

4/10/12 3:21 PM

A specific Forum dedicated to CDR System technical support with fixes, notices of bugs and patches, tips other solutions.

The Forum sections are set up for special access to "Verified Reconstructionists" which allows for discussions with technical contributors.

A public section allowing access and interaction with those outside the AR community.

F

or several years, the ARC Network supported a FAQs and discussion section and there have been other email groups on line serving as resources to crash reconstructionists providing access to the knowledge of others in the community. Many of these, particularly the email groups, have become, at times, the source of spam email blasts or users resorted to a "daily digest" that became unwieldy in its size with footers and repeats of the original message or simply and endless string of "me too!" messages. Moreover, while the it created an archive of messages, the individual email format created a series of individual messages that was, many times, difficult to search for narrow topics.

Other Forum-based communities have long been the method of choice for people with similar interests as varied as frequent flyers (i.e.: flyertalk.com), snowboarders, mechanics and those who favored a particular type of car (i.e.: Mazdaforum.com). Announced in January at the CDR User's Summit in Houston, the latest and most versatile player in the on line crash reconstruction community is CrashForum.Info at http://crashforum.info Where email groups are useful, particularly for small communities, an Internet forum, or message board, is an online discussion site where people can hold conversations in the form of posted messages called threads. It's different than a "chat room" because threads are archived and depending on the access level of a user a posted message might need to be approved by a moderator before it becomes visible. In the context of crash reconstruction, CrashForum.Info has been built to allow for crash reconstructionists to have a private area but still allow for the public - including lawyers and insurance professionals - to separately ask questions that might be answered by professionals in the crash reconstruction field. Clearly, for those on the private practice side of the house, that could lead to case assignment contacts.

36

Collision Magazine

www.collisionpublishing.com

http://www.CrashForum.info Unlike an email group, a forum has a tree-like in structure made of threads branching off a specific topic. And there's more: •

Rather than sifting thru unrelated messages, a reader can choose a topic, then see all the replies (and related documents) posted chronologically from the first post onward IN that thread.

•

Files, whether documents, photos or CDR files, can be posted IN that specific thread and are then readily available to the end user there rather than in a separate search.

•

There's no need for a constant daily barrage of emails from a group with endless footers and repeats of previous messages or daily digests which, similarly, become lengthy and almost unmanageable. CrashForum.info is set up as a true bulletin board giving you the flexibility to look through topics at your leisure marking those read or which you want to keep an eye on so your next review is more focused and direct.

•

You can reach out to another user directly with a "personal message" privately through the forum rather than trying to reach them via external email which may or may not get blocked as spam. After three posts, the personal message is available to all registered users of CrashForum.info.

•

Community access is still limited. At CrashForum. info you'll see that access to certain areas requires approval.

That means we'll still keep those not in the reconstruction community out of more specific, technical discussions.

Forget to sign a post? Not any more, all posts have automatic author identification.

In the addition to email capabilities from within the Forum, the "post reply" window allows for message formatting including special characters, text font highlighting and external web page linking as well as the ability to directly attach pictures, drawings, PDF files and CDR files within the post and where it will stay with the thread.

Over time, CrashForum. info will become an invaluable on line community resource for crash reconstructionists particularly with the introduction of the Accident Reconstruction Search Engine at www.arsearchengine.com www.collisionpublishing.com

Collision Magazine 37

Monte Carlo Method

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bstract

A

The Monte Carlo Analysis method has been shown to be a successful way to account for the uncertainty of input parameters in a crash reconstruction. The Monte Carlo Analysis (MCA) method evaluates the range of input parameters by selecting a random number between the adopted ranges and carrying that throughout the calculation. Evaluating the randomly selected input variables several

thousand times, results in a normal probability distribution of the analysis results. Descriptive statistics is then used to assess the minimum, maximum, and average results; or maybe a confidence level of what is more probability true than not (51%) or perhaps a range which constitutes a 90% confidence level, or any confidence level the investigator wishes to ascribe to the uncertainty analysis.

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0.5785 0.3343 0.7689 0.3466 0.8677 0.6299 Collision Magazine 39

One of the major purposes of reconstructing a vehicle collision is the estimation of vehicle speeds prior to or at the time of impact. Current technology includes the pre-crash and/or delta-v of a vehicle imaged from the vehicle’s airbag control module (ACM). In a multi-vehicle collision, the investigator may only have this data for one of the vehicles.

This article will discuss the application of the Monte Carlo Analysis method, and conditionally sampling or filtering the results consistent with the data retrieved from the Event Data Recorder (EDR). Then, a more narrow speed range or higher confidence level can be estimated for the vehicle which does not have the EDR capability.

I

ntroduction to the Monte Carlo Analysis Method

The name “Monte Carlo” applies to a class of mathematical methods which were first used by scientists working on nuclear weapons in the 1930’s and 1940’s.

In the 1930’s, Enrico Fermi performed numerical experiments on the behavior of the neutron, that would be called Monte Carlo calculations. The method was first coined in the 1940’s by scientists working on a nuclear weapons project (Manhattan Project) at the Los Alamos National Laboratory. Being secret, they code named the project “Monte Carlo”, in reference to the Monte Carlo Casino.

Advances in probability theory and random theory are the foundation of Monte Carlo theory. The Monte Carlo method is used in economics to nuclear physics to regulating traffic flow.

“

The Monte Carlo Analysis method (MCA) first appeared in accident reconstruction in two Society of Automotive Engineer (SAE) papers authored in 1994 40

Collision Magazine

The Monte Carlo Analysis method (MCA) first appeared in accident reconstruction in two Society of Automotive Engineer (SAE) papers authored in 1994. The first was authored by Kost & Werner1 titled “Use of Monte Carlo Simulation Techniques in Accident Reconstruction” and the second was authored by Wood & O’Riordain2 titled “Monte Carlo Simulation Methods Applied to Accident Reconstruction and Avoidance Analysis.” This method required the use of specific software that was able to generate hundreds of thousands of random numbers and perform thousands of calculations. The application of this method with common software that would handle this dilemma, appeared in a paper by Wade Bartlett3 in 2003, “Conducting Monte Carlo Analysis with Spreadsheet Programs”.

Since then, the use of the MCA method has gained momentum in the crash reconstruction community, and with the ever increasing Frye and Daubert hearings, may be the standard in future crash reconstruction. This article does not attempt to explain in detail the MCA method, but shows an extension of that method using data imaged from an Airbag Control Module. The Northwestern University Center for Public Safety, now offers the Crash Reconstruction III course, over 20 hours of which is devoted to the MCA method with original work performed by this author.

T

he Monte Carlo Analysis Method

In a typical crash reconstruction, such as the calculation of speed from a momentum analyses, it is often necessary to perform parametric analyses to determine the sensitivity of the results to changes in values used for the various input parameters1. The analyses are usually first performed using the “best available data” or best estimate of the input parameter. A sensitivity analysis is then performed by considering a range of the input parameter. One approach often seen is using the low and the high value for one variable. For example, performing a momentum analysis by only changing the post-impact speed estimate that was originally a low to high estimate, and then calling the resultant calculation a low and high estimate. This method does not address the uncertainty in the other variables, such as vehicle weight, approach or departure angles.

A variation on this approach is to use a high/low parametric range for all input variables, then performing the calculation using the combination that would produce the low estimate and the combination that would produce the high estimate. There are two disadvantages with this type of analysis. One, it usually produces unrealistic results or a large range for the estimate; and two, the probability that all the variables line up at the same time to produce the low or high result is actually very close to zero. The MCA

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method is a good way to perform the sensitivity analysis without these disadvantages.

It is then possible to obtain quantitatively the probability that the results are within a selected range. It also provides a rational basis for dealing with uncertainty and ranges of parameters in the crash reconstruction analysis.

Why consider using this technique? The MCA method addresses the issue of uncertainty (the presence of variations in the input variables). Reasonable ranges of input values can be estimated and considered for all input values, and not just one or a few. Not all low values or high values of input ranges are expected simultaneously. And finally, it establishes a numerical confidence interval.

The MCA method is based on the use of random numbers and probability statistics.

For example, the conservation of momentum analysis in an angled collision has eight independent variables and one dependent variable. The eight independent variables are

Figure 1: Excel’s© Random Number Generator in this example will generate 105,000 random numbers that will be uniformly distributed.

Figure 2: The generated random numbers are distributed in seven columns and 15,000 rows.

the post-impact speed, vehicle weight, approach angle, and departure angle for each vehicle. Each dependent variable, except for the approach angle of Vehicle 1, could have a high and low range. These are based on reasonable estimates for each input variable. The random component of the MCA method randomly chooses a discreet value between each range, for each variable, and performs the calculation one at a time, thousands of times. A statistical analysis is then performed on the results which would give a range that is within a certain confidence interval, ascribed to by the reconstructionist. If the range is to be within a reasonable degree of certainty or more probably true than not, that range typically has a confidence interval of 51%. Or that the results fall within 51% of the mean.

That range then is within a reasonable degree of scientific certainty, which has been defined by the court.

Figure 1 is the Random Number Generator found in the spreadsheet program Excel©. Figure 1 will generate 105,000 random numbers on the interval between 0 and 1, (15,000 random numbers for seven variables). The random numbers generated for seven variables is shown in Figure 2. Employing this in the conservation of momentum analysis method for an angled collision, would calculate 15,000 results for Vehicle 1 and Vehicle 2 each. The statistical analysis is then applied to the 15,000 results for each vehicle. The results are tallied into a histogram similar to what is shown in Figure 3.

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Collision Magazine 41

Figure 3: Histogram for a vehicle speed analysis.

An extension of the MCA method is conditional probability. That is, what would be the probability of Event B given Event A? Or, what would the speed range of Vehicle 1 be given the speed range of Vehicle 2, based on the imaging of the Airbag Control Module (ACM). This approach is illustrated with the following case study, which will also show the results when using the “best available data,” and performing each of the described analysis methods for sensitivity.

C

ase Study: Conservation of Linear Mo- Figure 6 is a post-crash situation diagram showing the vehicles at final rest and the results of the crash on the road. mentum

The crash involved a 2003 Ford E-350 van (ambulance) and a 2002 Chevrolet Malibu (Malibu) that collided on June 4, 2005. The ambulance was traveling north on Ridge Avenue in Evanston, Illinois approaching a traffic signal controlled intersection with Dempster Avenue. The Malibu was traveling west on Dempster Avenue and was struck in the driver’s door by the front of the ambulance in the middle of this intersection. The driver of the Malibu died as a result of injuries received in the crash which occurred at 3:27 a.m. Figure 4 and 5 are scene photographs of the crash taken by the Evanston Police Department.

The issues addressed within the crash reconstruction concerned the speed of the ambulance and Malibu, the time/ distance relationship between the vehicles, and the sightline of each driver approaching the impact area. The Malibu 42

was equipped with an ACM that recorded the longitudinal change in velocity experienced by the Malibu during the collision, and five seconds of pre-crash data, typical for a GM product of this year. The Malibu was coming out of a 25 mile per hour speed zone into a 30 mile per hour speed zone. The ambulance was in a 30 mile per hour speed zone, and was returning back to quarters after having dropped off a patient and was not operating with emergency lights and siren activated.

Collision Magazine

The ambulance position was estimated because the driver had moved the vehicle from its rest position prior to police arrival. This crash presented some unique aspects to the momentum impact speed calculation. Those included: • Final rest of the ambulance is unknown, but there is some evidence to support that location, • There was a secondary “slap” impact between the vehicles post-impact, • The Malibu struck and drove on top of rock ornaments and struck a street light after impact, • Speedometer on the Malibu was stuck at 23ish miles per hour, and • There was event data from the Malibu (longitudinal delta-v and pre-crash info).

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Figure 4: Crash scene photograph (Source: Evanston Police Department, IL).

Figure 5: Crash scene photograph (Source: Evanston Police Department, IL).

Figure 6: Crash scene diagram.

The conservation of linear momentum (COLM) was employed in this angled collision to estimate the vehicle speeds at impact. Figure 7 is an estimate of the vehicle’s position at impact, path to final rest, and at final rest. The diagram also shows the angle of approach and departure range for each vehicle. Table 1 shows the “best estimate” for the variables used in the COLM calculation. When the calculation is performed using the “best available data,” the speed of the ambulance is estimated at 29.0 mi/hr, and 23.6 mi/hr for the Malibu.

Next, the range for the post-impact speed was estimated at 20.5 to 26.3 mi/hr for each vehicle, shown in Table 2.

This was estimated by calculating the post-impact speed of the ambulance and applying the common velocity assumption to the Malibu. When the calculations were performed using this range, the speed of the ambulance was estimated at 25 to 32.2 mi/hr (7.2 mi/hr difference) and 20.4 to 26.2 mi/hr (5.8 mi/hr difference) for the Malibu.

However, with this process the uncertainty of each variable is not accounted for.

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Collision Magazine 43

Figure 7: Crash scene reconstruction diagram.

Table 1: Best estimate input variables for a COLM analysis.

Table 2: Range the post-impact speed variable for the COLM analysis.

The process was repeated by estimating a reasonable range for each variable. This is shown in Table 3. The combination of variables that would give the high and low speed for each vehicle was used in the COLM calculation. When that was done, the speed of the ambulance was estimated at 23.6 to 33.5 mi/hr (9.9 mi/hr difference) and 16.2 to 33.3 mi/hr (17.1 mi/hr difference) for the Malibu. Here is where the disadvantages manifest themselves. The difference in the range is large, especially for the Malibu, which

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Collision Magazine

is by far the lighter of the two vehicles. Each end of the range, and any number between, has the same probability (uniformly distributed). When in reality, the probability for each end of the range is close to zero. That is, the probability is close to zero that the combination of all the variables in the collision line up at the same time to produce those results that occur at each extreme end of the range.

The MCA method was then applied to the same input ranges and is shown in Table 4. The post-impact speed for

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Table 3: Range of input variables for the high/low COLM analysis.

Table 4: Range of input variables for the MCA and COLM analysis.

Vehicle 2 was changed so that it would always be greater than or equal to the post-impact speed of Vehicle 1, by a value of the coefficient of restitution (e). This allowed for an input range of the coefficient of restitution. In this case study, the range was estimated at 0 to 0.1. Then, for each vehicle, 15,000 calculations were performed. When that was done, the speed of the ambulance was estimated at 26.4 to 31 mi/hr (4.6 mi/hr difference) and 21.5 to 27 mi/hr (5.5 mi/hr difference) for the Malibu, which is within one standard deviation. The confidence interval for this estimate is 68%, or that 68% of the results lie within the mean or average speed. Compare this range with that previous calculated with the added benefit that the uncertainty of each variable has been accounted for.

Sometimes probabilities need to be reevaluated as additional information becomes available, in this case, the Malibu’s ACM download. A useful way to incorporate ad-

ditional information into a probability model is to assume that the outcome that will be generated is a member of a given event4. This event, say A, defines the conditions that the outcome is known to satisfy. This data is from the ACM download. Then probabilities can be revised to include this knowledge (the ACM download). The probability of an event B under the knowledge that the outcome will be in event A is called the conditional probability of B given A. Simply, the conditional probability of the ambulance speed (event B), given event A, the ACM download.

The conditional probability as an extension of the MCA method was then employed which included the results of the ACM download of the Malibu. The Malibu’s ACM showed that it was traveling at a steady by gently rising speed of 22-22-22-23-23 mi/hr during the five seconds prior to the collision (shown in Figure 8). The speed for the Malibu is calculated first before the ambulance speed

Figure 8: CDR Report from the Malibu. www.collisionpublishing.com

Collision Magazine 45

Figure 9: Spreadsheet calculations for the Malibu.

“

Figure 10: Spreadsheet calculations for the ambulance.

The conditional probability as an extension of the MCA method was then employed which included the results of the ACM download of the Malibu.

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Collision Magazine

is calculated. The calculated speed of the Malibu is carried through for the ambulance speed calculation. The MCA method and COLM calculation method allows the speed calculated from the Malibu (Vehicle 2) to be filtered before the ambulance (Vehicle 1) speed is calculated. Due to uncertainties in speed and timing of the ACM download, it was decided to only accept calculations which produced an impact speed of 22 to 25 mi/hr for the Malibu. In other words, those speeds calculated for the Malibu that are not between 22 to 25 mi/hr, will be filtered out. Those filtered speeds are not carried through for the ambulance speed calculation. In the spreadsheet, the cell is blank for the speeds that are not within that range (Figure 9). Therefore, nothing is carried through to the ambulance speed calculation and its cell will be blank (Figure 10). When the statistical analysis is applied to the results, the blank cells are not counted.

Limiting the calculations to only those results which met the filtered speed range of the Malibu (22 to 25 mi/hr), we get approximately 3800 accepted scenarios per 10,000 trials. This conditionally sampled result shows the ambulance’s impact speed to be 26.5 to 30.8 mi/hr, a difference of 4.2 mi/hr, within a confidence interval of 68% given the ACM download. Therefore, the conditional probability within a confidence interval of 68% (one standard deviation) would

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state that the speed of the ambulance is 28.6 ± 2.47 mi/hr, given the speed of the Malibu, 23.5 ± 1.5 mi/hr.

This crash was also simulated in HVE’s - EDSMAC4© to verify the speeds calculated against the longitudinal deltav downloaded from the Malibu’s ACM. The first-contact position used in the simulation is shown in Figure 11. The Malibu’s ACM indicated a longitudinal delta-v of -8.01 mi/hr at 145 ms after AE. The simulation converged on an impact speed for the ambulance at 27.8 mi/hr, and 22.6 mi/hr for the Malibu. Both within the range previously estimated in the MCA method. The longitudinal delta-v (uvel) for the Malibu in this simulation was -7.9 mi/hr over 150 ms, very close to that reported by the Malibu’s ACM.

C

and/or lateral delta-v, principal direction of force (PDOF), can also be extended to this analysis by using this data as a criterion to calculate the other vehicle’s speed. This process also demonstrated the need for a complete reconstruction before relying on the ACM download. It wasn’t until after the complete reconstruction was performed, that the ACM data was incorporated into the analysis, and to be confident that the ACM data matches the crash signature.

R

onclusion

This article demonstrated the use of conditional probability in a Monte Carlo Analysis. The Monte Carlo Analysis technique was briefly explained. The method has been shown to be a successful way to account for the uncertainty of input parameters. A case study involving a vehicle with a recorded deployment event and one without was used to narrow the speed estimate of both vehicles. This was accomplished with the Monte Carlo Analysis method and using the Malibu’s ACM download as a criteria to estimate the ambulance speed. The speed of the Malibu, recorded by the ACM, was used to filter its speed before it was carried through for the ambulance speed estimate. The ACM data including longitudinal

eferences 1. Kost, Garrison, and Werner, Stephen, “Use of Monte Carlo Simulation Techniques in Accident Reconstruction,” Society of Automotive Engineers, SAE Paper No. 940719, 1994.

2. Wood, Dennis P., and O’Riordain, Sean, “Monte Carlo Simulation Methods Applied to Accident Reconstruction and Avoidance Analysis,” Society of Automotive Engineers, SAE Paper No. 940720, 1994.

3. Bartlett, Wade, “Conducting Monte Carol Analysis with Spreadsheet Programs,” Society of Automotive Engineers, SAE Paper No. 2003-01-0487, 2003.

4. Montgomery, D.C., and Runger, G.C., Applied Statistics and Probability for Engineers, Fourth Edition, John Wiley & Sons, Inc., Hoboken, NJ, 2007.

Figure 11: First-contact position in the HVE-EDSMAC4© simulation. www.collisionpublishing.com

Collision Magazine 47

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Collision Magazine

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H

ow accurate is a Toyota EDR??

While Toyota first began phasing in EDR’s in the 2001 model year, until April 2010 there was only one prototype tool set in the entire United States that could read the data.

Toyota posted on its website and reiterated through its engineers being deposed in legal cases that the prototype tool set had not been validated. That didn’t mean the data was inaccurate, it simply meant it was not fully validated. This article is a compilation of research data available on Toyota EDR accuracy in chronological order. This article will focus on the parameters of greatest interest to accident reconstruction, the change in velocity at impact or “deltaV”, and precrash data such as speed, brake, accelerator pedal position, and RPM.

Toyota vehicles, and the researchers asked Toyota to read any data available from the airbag control modules of the three 2004 model Toyota vehicles from that group. The study published graphs of the EDR delta-V versus time data plotted side by side with the reference instrumentation. NHTSA was concerned some EDR’s did not record for the entire crash pulse duration so they reported EDR Delta V relative to instrumentation at 100ms in to the crash pulse even if the crash lasted slightly longer. Test 4846 was a 35 mph barrier crash of a 2004 Sienna. The instrumentation reported 39.8 mph at 100ms compared to the EDR at 38.3, a -3.8% error. Test 4855 was also a 35 mph barrier, of a 2004 Solara. Instrumentation reported 38.9 @100ms while the EDR reported 36.3, a -6.7% error. Test 5071, a 25 mph barrier of a 2004 Camry, saw instrumentation report 27.6 mph @ 100ms while the EDR reported 27.9 mph, and error of +1.1%.

“Evaluation of Event Data Recorders in Full System Crash Tests” 2005, ESV 05-0271

The authors did not summarize by manufacturer, they gave an average for all manufacturers put together. Strangely, the authors took the absolute value of the errors before averaging them. For this series of tests, the average Toyota EDR using signed values is -3.3% with a range of -6.7% to +1.1%. Not addressed in this study as it may relate to a “percent difference,” for Toyota or other vehicles, was the difference between the control instrument delta-V for the entire crash pulse versus the EDR-based data which began at the module’s trigger threshold.

The first known study of EDR accuracy that included any Toyota vehicles was published by Niehoff in 20051.

This study examined 37 NHTSA instrumented crash tests from GM, Ford, and

Niehoff 2005 2004 Sienna 2004 Solara 2004 Camry

Impact mph 35.0 35.0 35.0

EDR Speed 34.8 NA NA

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Actual Speed 35.1 NA NA

EDR rV 38.3 36.3 27.9

Ref Inst rV % rV Error 39.3 -2.5% 38.9 -6.7% 27.6 1.1% Collision Magazine 51

“Preliminary Evaluation of Advanced Air Bag Field Performance Using Event Data Recorders” The second known study was published by Gabler in 2008.2 This study took 44 NHTSA and 4 IIHS crash tests and compared EDR delta-V and precrash speed data (where available) to reference instrumentation. The tests were primarily full frontal rigid barrier tests at 35 mph (37/48) but included small samples of other test modes, including 5 side impacts. There were 15 Toyota tests, 11 full frontal barriers, and 4 side impacts. Since side impacts have very low or no longitudinal acceleration, and the early EDR’s measured only longitudinal acceleration, side impacts will be excluded from any accuracy summaries. Gabler did not separate results by manufacturer. Looking only at the 11 frontal Toyota tests, the Delta V averaged -1.6% with a range of -21.8% to +11%. Test 5239, the -21.8%, was a 2005 Tundra which tracked perfectly for the first 60ms then the EDR suddenly quit accumulating additional delta-V. The original paper did not discuss possible causes but this author believes it is likely that actual acceleration G’s likely exceeded the 50G EDR sensor capability and clipped the data resulting in under-reporting the Delta V. “Study on Characteristics of Event Data Recorders in Japan” 2009 The third known study was published by Takubo in 2009.

Toyota is a global manufacturer, and the Japanese National Research Institute of Police Science also tested EDR’s.

Takubo published SAE 2009-01-0883 “Study on Characteristics of Event Data Recorders in Japan” 3 with data on 17 crashes, 7 full frontal barrier, 7 offset deformable barrier, and 3 “real world” crashes. For the barrier tests the paper does not list specific models or model years tested,

52

but indicate the vehicles are Toyotas. The 3 real world tests were on Corollas. Takubo used 3 different types of reference instrumentation: video analysis, a centrally placed accelerometer, and the average of two accelerometers placed on the left and right frame rail. Having 3 different sets of reference instrumentation gave 3 different accuracy measurements for the EDR, but of the three, this author believes that the video analysis is the most representative.

Measurements were reported in metric units, meters per second. Compared to the video analysis, Takubo found EDR Delta V for the 14 barrier tests to under report delta-V by an average of -7.3% with a range of -12.9% to +0.5%, with an RMS of 8.5%.

Precrash speed data for the 14 barrier tests averaged the EDR reporting -1.8%, ranging from -3.4% to +3.4%. The RMS was reported as 2.6%.

The first real world crash was a rigid offset barrier. The pre-crash speed data had a difference of -0.6% and delta-V was reported within 0.3 m/s on a base of 20 m/s, which is approximately -2%.

The second real world crash was an intersection collision, front corner to front corner. The difference in the EDR pre-crash speed for both vehicles was +1.3%. The deltaV error was reported as 4% for one vehicle and 10% for the other, with no sign given. Graphs of both X and Y delta-V time histories versus reference instrumentation were shown and from the graphs, it would appear the EDR under-reported the reference instrumentation, meaning the sign should be negative. The percentages shown are presumably for the resultant total delta-V (square root of X squared plus Y squared).

Impact

EDR

Actual

EDR

Ref Inst

rV %

Gabler 2008

mph

Speed

Speed

rV

rV

Error

2005 Camry 5283 2005 Corolla 5160 2005 Matrix 5209 2004 RAV4 4893 2005 Sienna 5269 2005 Tacoma 5312 2004 4Runner 5037 2004 Prius 4933 5218 Tundra 5218 2005 Tundra 5239 2005 Scion TC 5217

35.1 35.1 35.1 35.3 35.0 34.9 34.9 35.4 35.0 35.0 34.9

NA NA NA NA 33.8 NA NA NA NA NA NA

35.3 37.6 37.4 40.3 36.5 34.4 38.5 42.0 36.0 29.5 43.4

36.2 38.3 38.0 37.3 38.2 36.9 38.5 38.7 38.4 37.7 39.1

-2.5% -1.8% -1.6% 8.0% -4.5% -6.8% 0.0% 8.5% -6.3% -21.8% 11.0%

AVG

-1.6%

Collision Magazine

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The third real world crash simulated a multiple rear end collision. “Car 1” is stopped, “car 2” hits the back of “car 1,” and then “car 3” hits the back of “car 2” causing it to hit the back of “car 1” again. “Car 2's” speed at impact was measured by an optical speed sensor at 8.5 m/s while the EDR last data point before impact during heavy braking was 11.1 m/s, a 31% difference, but possibly explained by being measured at different points in time during a dynamically changing speed. For “car 3,” the optical speed sensor reported 21.5 m/s while the EDR last data point with no braking taking place was 21.7 m/s or +1%. The paper also attempted to analyze the secondary impacts which were at very low speeds, and due to sensor resolution obtained distorted high percentages of error.

NHTSA-NVS- 2011-ETC-SR07 “Event Data Recorder – Pre Crash Data Validation of Toyota Products” In this fourth study, NHTSA conducted a specific evaluation of Toyota pre-crash data accuracy. 4 No specific authors were listed. The paper reports 28 staged events using two 2007 Camry’s and a 2008 Highlander as bullet vehicles striking the back of a 2006 Tacoma target vehicle with a 2-5 mph closing speed in order to create a nondeployment (“trigger”) event the EDR would capture.

NHTSA defined the vehicle speed tolerance as +/- 1.5 miles per hour, and was aware that the recorder speed temporary buffer only refreshed speed data every 0.5 seconds.

Around each EDR data point represented by a blue dot they created a “window” of acceptance of +/- 1.5 mph that started at the blue dot and extended back in time 0.5 seconds. If the red GPS speed data crossed anywhere in the box, then they deemed it was within the acceptable tolerance. NHTSA concluded that 100% of the precrash speed data fell within the tolerance and time window. A sample graph is shown in figure 1.

NHTSA did not explain how they synchronized the data between the EDR and the reference instrumentation.

In the graph above, the time of the last EDR data point sampled closest to algorithm wake up is reported relative to the “-1” data point with a resolution of 100ms (0.1 seconds). One would expect algorithm enable and that last data point to be plotted within 0.1 seconds of the beginning of the discontinuity in speed caused by the staged impact; however, the speed graph shows the speed change from the impact not beginning until about 0.2 seconds after the last data point. In “Camry 4D test 1 strike 2” below, the last data point is plotted about 0.2 seconds after the speed discontinuity instead of within 0.1 second before. In addition, the EDR data that would be labeled “-5” in the Toyota Read Out Tool (ROT) report is not shown on the data graph. It would be at the left edge of the graph and the tolerance window would extend off to the left of the graph by 0.5 seconds.

Figure 1: Camry 4D Validation Test 1 (Vehicle Speed)

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Collision Magazine 53

Figure 2: Camry 4D Validation Test 2 (Vehicle Speed)

However, in reviewing the data, a different synchronization would not have affected the conclusions significantly.

The NHTSA acceptance criterion was that the red reference instrumentation data line fell within the tolerance box drawn behind each EDR data point. No data table was reported. Without a data table and a more precise synchronization it is difficult to calculate a specific error rate. The conclusions must therefore be limited to an acceptance that there is no reason to believe the speed data is not accurate to within the stated tolerance of +/- 1.5 mph. Given the tests were conducted at approximately 25-30 mph, +/- 1.5 mph on a base of 30 mph would correspond to approximately +/-5%. EDR speed data is generally believed to be accurate within +/-4% based on the design tolerances suggested for GM vehicles in the 1999 Chidester paper and many specific models tested have been within 1%.

Similarly, the accelerator pedal voltage had a refresh rate of 512 milliseconds and was given a tolerance of +/- 0.1 volts.

100% of the test data fell within this tolerance window. A sample acceptance graph is shown in figure 3.

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The brake on/off was reported instantaneously and had no tolerance since it was either reported as on or off. 100% of the brake on/off data was accurate within the range specified.

The NHTSA report lists 14 vehicle to vehicle crashes, which it says generated 28 events (presumably one in the bullet and one in the target for each collision), but the report only gave data for the 14 impacts of the bullet vehicles. No target data was reported.

“Study on Characteristics of Event Data Recorders in Japan; Analysis of J-NCAP and Thirteen Crash Tests”.

Takubo SAE 2011-01-0810 In this fifth study, this paper repeats the data from Takubo’s 2009 paper but adds 14 additional J-NCAP tests (7 more full overlap barrier and 7 offset deformable barrier tests).

For speed data, the average of the 14 full overlap barrier test EDR data was -2.0% of the reference instrumentation, with a range of -6.3 to +1.9%, and an RMS of 2.6.% For the 14 ODB tests, the EDR was -2.1% of reference instrumentation, with a range of -4.1% to 0.0% and an RMS of 2.7. Putting both full overlap and ODB tests together, the EDR averaged -2.1% of the reference instrumentation, with an RMS of 2.6.

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Figure 3: Camry 4D Validation Test 1 (Accelerator)

Figure 4: Comparison results of pre-crash velocity in J-NCAP FLB and ODB crash tests

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Collision Magazine 55

Figure 5: Comparison results of post-crash maximum delta-V in J-NCAP FLB and ODB crash tests

For the full frontal barrier tests, the EDR delta-V data averaged -9.1% of the reference instrumentation with a range of -14.7% to 0.

actual data of -2.27 to +0.20 is certainly very close to the expected range even if there were no minor measurement errors.

For the offset deformable barrier tests, the EDR Delta V ranged from -20.5% to +11.0%.

Each test had belted driver and passenger, and in each test the EDR correctly reported the occupants as seat belted.

“Event Data Recorders in Toyota Vehicles”

The change in velocity versus time was plotted from high quality crash test instrumentation versus the EDR data.

The 14 tests averaged -2.39km/h (-1.5 mph) low, with the range from +0.47 to -8.84 km/h (+0.3 mph to -5.5 mph).

This equates to an average of -4.09% and a range from -14.05% to +0.89%.

Prior to this sixth study, “Crash Pulse Data From Event Data Recorders In Rigid Barrier Tests” German, A., Dalmotas, D., & Comeau, J., was published in ESV 11-0395, with data from multiple manufacturers including 7 Toyota crash tests ranging from 40 to 55 km/h. That same data and additional Toyota data is included in this article by the same authors, and the other manufacturers are not mentioned. It was reported at the 21st Canadian Multi-Disciplinary Road Safety Conference in Halifax, NS in May, 2011. 6 They compiled data from 14 full frontal rigid barrier tests at 40, 48, and 56 km/h (25, 30 and 35 mph). The models tested included 3 from 2005 Camry, 5 from 2009-10 Corolla, 5 from 2009 Toyota Matrix, and 1 from a 2009 Venza.

The Toyota EDR speed data averaged 1.65 km/h (1.0 mph) low, and considering Toyota’s stated data limitations that speed is reported to the next lower (even number) 2 km/h, values 0-2km/h low should be expected, and the 56

Collision Magazine

The article noted that two of the higher speed (56 km/h or 35 mph) tests were anomalous in that they had more negative errors than the rest of the group of tests. This author would offer a possible explanation that some peak acceleration values in such a severe test as a 35 mph full frontal barrier may have exceeded the maximum value that could be captured by the crash sensor. When NHTSA proposed accuracy requirements in 49CFR563, manufacturers specifically objected citing that sensor “clipping” was possible in some tests. NHTSA has explicitly recognized this in their August 5, 2011 response to petitions for reconsideration, and granted relief to accuracy requirements if sensor clipping is documented.

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Figure 6: Test summary for 14 frtonal rigid barrier tests

Figure 7: Compiled data from 14 frontal rigid barrier tests

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“Testing and Analysis of Toyota Event Data Recorders”

By Exponent, October 11, 2011

classification, and transmission gear selector position. All parameters reported accurately.

This seventh study funded by Toyota consisted of 7 staged collision full frontal barrier crashes and 224 additional non-deployment events, gathered from a total of 24 vehicles, reported in a 72 page report found on Toyota’s website. 7 Model years ranged from 2002 to 2010 and models included Avalon, Camry, Corolla, FJ Cruiser, Prius, RAV4, Sienna, Tacoma, Tundra, Lexus ES350, and Lexus IS 350.

“Speed Accuracy of Event Data Recorder in the 2010 and 2011 Toyota Camry During Steady State and Braking Conditions” SAE 2012-01-0999

Of the 7 crash tests three had EDR’s capable of recording precrash data. The precrash vehicle speed data was reported correctly within the data limitations (19.9mph EDR vs 19.6 mph reference instrumentation on the 2007 RAV4, 29.8mph EDR vs 30.2 mph reference instruments on a 2007 Tundra, and 48.5 mph EDR at 400ms to impact vs 50.3 mph at impact on a 2007 Lexus ES350.)

The EDR delta-V was reported for each of the 7 crash tests versus reference instrumentation. Three of the tests were so severe (full frontal barriers at 50mph, 45 mph, and 40 mph) that the 50G capability of the EDR accelerometer was exceeded, and as a result the EDR under-reported the total delta-V. In the other 4 tests the delta-V reported ranged from +0.6% to +6.8%, well within the normal +/10% design accuracy tolerance attributed to EDR delta-V data in prior papers.

Yellow color code indicates that reference accelerometers indicate the 50G EDR sensor limit was exceeded such that Delta V would be expected to be under-reported.

The 224 additional non-deployment tests were used to evaluate static parameters such as driver and front passenger seat belt use, driver seat position, passenger occupant

This eighth study was published April 2012 by SAE. 8 The data was gathered in June 2011 by Richard Ruth (author of this article), Dr. Jeremy Daily, and Wade Bartlett. The authors drove 2010 and 2011 Toyota Camry vehicles under controlled conditions with reference instrumentation, and then created EDR recordings by a modest impact on the airbag control module which had its bolts loosened similar to the nondeployment events created in the Exponent study.

A Racelogic VBOX was used as a data acquisition system recording speed from differential GPS sensors and vehicle CAN data for speed, brake on/off, and accelerator pedal position. On some test runs, an accelerometer was added on the airbag control module to show the impact that triggered the EDR, to allow some measure of synchronization between the EDR and the VBOX data.

Multiple test runs were completed at 50 mph and 70 mph on each of two test vehicles, in both steady state and maximum braking conditions.

Preliminary results indicate the speed data under steady state conditions is accurate within the resolution reported in the data limitations. Under dynamic heavy braking conditions the speed may be over or under reported depending on when the speed data was buffered. Since the vehicle is slowing down over time due to braking, data points put in the RAM buffer up to 0.5 seconds before being sampled by the EDR will report higher speeds than the vehicle is

Exponent Toyota Barrier Crash Testing Summary

2007 RAV4 2007 Tundra 2007 Corolla 2005 Camry 2007 Lexus ES350 2002 Camry 2003 Camry

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Impact mph

EDR Speed

Actual Speed

EDR rV

Ref Inst rV

17.1 30.2 40.6 29.5 50.3 29.7 40.1

19.9 @ -0.3 29.8

19.6 @ -0.3 30.2

48.5 @ -0.4

50.3 @ imp.

20.5 33.5 43.9 34.7 33.9 34.6 34.9

20.3 33.3 45.3 33.0 53.9 32.4 40.7

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going at the time of the sampling. Data points buffered just before being sampled by the EDR may under-report the speed slightly due to wheel slip during braking.

“Evaluation of Camry HS-CAN Pre-Crash Data”, SAE 2012-01-0996 This ninth study was published in April 2012 by SAE. 9 The data was gathered by Roger Brown and Samuel White.

The authors drove a 2010 Toyota Camry under controlled conditions with reference instrumentation, and then created EDR recordings by a modest impact on the airbag control module (without the bolts loosened).

A Race Technology DL1 Mk3 was used as a data acquisition system recording speed from GPS sensors and vehicle CAN data for speed, brake on/off, engine speed and accelerator pedal position. Also recorded by the data logger was GPS sensor data, brake pedal switch voltage, accelerator pedal positions sensor voltage, and crankshaft position sensor data. There was a contact switch on the end of the tap bar that was used to allow precise synchronization between the EDR and the data logger.

Recordings were made during maximum acceleration, steady state speed and maximum braking.

The CAN speed data under steady state conditions was found to under report by 2.7%. Under steady state speed conditions the standard deviation ranged from .21 to .73 km/h. The CAN speed data was observed to under report by an amount up to the resolution of the speed data, 1 km/h. The CAN truncates the speed and rounds down to increments of 1 km/h. This appears to hold true during heavy braking and maximum acceleration. During maximum braking and acceleration, there were brief periods of tire slip. The CAN data faithfully reported the tire speed, which briefly over or under reported the vehicle speed.

Similar methods were used to analyze the CAN data for engine speed, accelerator pedal position and brake status.

The refresh rate, resolution and difference to independent instrumentation of each of the pre-crash parameters were determined “Confirmation of Toyota EDR Pre-crash Data” SAE 2012-01-0998 This tenth article by Roger Brown, Collision Research & Analysis, Inc.; Lance Lewis, Barry Hare, Mark Jakstis, Robert Landis, Harold Clyde, Ryan Buetzer, Toyota Motor Sales, U.S.A., Inc. was published in April 2012 by SAE. 10 The authors drove a 2011 Camry, 2010 4Runner and 2011 Prius under controlled conditions, and created EDR recordings by a modest impact on the airbag control mod-

ule that had its bolts loosened similar to the nondeployment events created in the Exponent study.

A Race Technology DL1 Mk3 data logger was used as a data acquisition system recording speed from differential GPS sensors and vehicle CAN data for speed, brake on/ off, engine speed and accelerator pedal position. The status of a contact switch was logged to show the impact that triggered the EDR, to allow precise synchronization of the data between the EDR and the data logger.

Multiple tests were performed on each vehicle including, stationary, accelerating, braking and constant speed from 0 to 96 km/h. The Toyota EDR appears to have recorded the data accurately.

T

oyota Data Limitations In Bosch CDR Reports

The Data Limitations which print in every Bosch CDR report indicate that speed data can only be displayed up to a maximum of 75 mph (122 km/h) in 04/06 EDR’s, and to 78 mph (126 km/h) in 00/02 EDR’s. Speed data is recorded in km/h and truncated to the next lower even number. None of the tests conducted up to this point in time have exceeded the maximum speed reportable by the EDR. Legal Case Admissibility Precedents: McAlonan V Tracy, Superior Court of New Jersey, Appellate Division, 2010 McAlonan was the driver of a 2003 Toyota Echo going through an intersection when Tracy’s 91 Explorer went through a stop sign and hit the Toyota in the side, tragically rendering McAlonan a quadriplegic. He sued Tracy, and Toyota, claiming the Toyota airbag was defectively designed and did not protect him during the crash. At the end of the trial, the jury found Tracy negligent, and reached a decision Toyota was not negligent. The physical evidence showed McAlonan was not wearing his seat belt.

During the trial, both sides had accident reconstructionists who had similar opinions of the vehicle speeds based on physical evidence. The Toyota EDR did not contain precrash speed data, but did contain data about how much the speed changed during impact (delta-V). Since the EDR data was not necessary to arrive at the impact speed, and the speed was not at issue between the two sides’ experts, the trial court decided not to allow testimony about the EDR data since it would take time to introduce it and explain it even though it did not appear to add value to the suit at hand.

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Collision Magazine 59

McAlonan appealed the verdict holding Toyota not negligent, and his attorneys cited a laundry list of rulings the judge made allegedly in Toyota’s favor. One item on the long list claimed that the judge erred by not excluding one of Toyota’s expert’s opinions because it allegedly disagreed with the event data recorder. The appeals court upheld the original trial court’s decision to allow the Toyota expert’s report to stand.

Michigan vs Bojaj, Wayne County, Michigan 3rd Circuit Criminal Court, 2011 A Lexus LS430 was alleged to be traveling at a high rate of speed when it impacted the rear of a Dodge Intrepid resulting in death to the Dodge driver. The EDR had the capability to record speed but due to a data limitation would not record speeds over 78mph. The delta-V data from the EDR was used in conjunction with other scene evidence to show the Lexus was traveling over 120 mph. A Toyota representative testified at trial that he read the data for law enforcement using the proprietary Toyota read out tool set and that the data was reliable.

Errors in Earlier Data Interpretations (Subsequently Corrected) Earlier versions of the Toyota ROT and the first Bosch Crash Data Retrieval System release, in a limited and specific set of part numbers, had a scaling error in the software resulting in the software over-reporting Delta V by 20%.

As soon as a sufficient number of modules had been read out, this was discovered and has subsequently been corrected. This earlier error in interpretation may explain some of the earlier test results where delta-V was over-reported.

data could only be accessed by Toyota’s internal Read Out Tool (ROT) set, and until 2010, there was only one such tool set in the United States and it was not fully validated.

Subsequent to that, Toyota made over 150 ROT sets available in North America and has released the ability to read all models to the Bosch Crash Data Retrieval System used by over 2,000 safety researchers, law enforcement, and private accident reconstructionists. Ten studies have been published which indicate the Toyota deltaV and precrash speed data is just as accurate as data from EDR’s found in GM, Ford, and Chrysler vehicles which have been used by accident reconstructionists for nearly 10 years

R

eferences:

1. Niehoff, P et al, “Evaluation of Event Data Recorders in Full System Crash Tests” 2005,ESV 05-0271

2. Gabler, C et al, “Preliminary Evaluation of Advanced Air Bag Field Performance Using Event Data Recorders”, DOT HS 811 015 3. Takubo, “Study of Characteristics of Event Data Recorders in Japan”, SAE 2009-01-0883 4. NHTSA-NVS- 2011-ETC-SR07 “Event Data Recorder – Pre Crash Data Validation of Toyota Products” (no author listed) published on the NHTSA website in spring 2011.

5. Takubo, “Study on Characteristics of Event Data Recorders in Japan; Analysis of J-NCAP and Thirteen Crash Tests”, SAE 2011-01-0810 6. Comeau, J.L., et al, “Event Data Recorders in Toyota Vehicles”, Proceedings of the 21st Canadian Multidisciplinary Road Safety Conference, Halifax, Nova Scotia, May 8-11, 2011.

7. Exponent, “Testing and Analysis of Toyota Event Data Recorders” October 11, 2011 8. Ruth, R et al “Speed Accuracy of Event Data Recorder in the 2010 and 2011 Toyota Camry During Steady State and Braking Conditions”, SAE 2012-01-0999 9. Brown, R. et al, “Evaluation of Camry HS-CAN PreCrash Data”, SAE 2012-01-0996 10.

Brown, R et al, “Confirmation of Toyota EDR Pre-crash Data”, SAE 2012-01-0998

Pictured above is the Crash Data Retrieval Tool www.cdr-system.com

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ummary/Conclusions

Since 2001 model year, Toyota has had some models with event data recorders. Initially the

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Becoming an Effective Expert Witness Collision: Volume 7 Issue 1

William J. Melkonian

Becoming an Effective Expert Witness William J. Melkonian Assistant District Attorney Essex District Attorney’s Office, Salem Massachusetts [1]

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uch emphasis is placed on ensuring that EDR evidence and the opinion testimony surrounding it are admitted at trial. The proponents of this evidence spend countless hours developing foundation questions, which will establish the reliability of box data, and eliciting a witness’ training and experience, which will allow the witness to testify as an expert. Every day, attorneys file countless memos and judges conduct innumerable Frye and Daubert hearings, all relating to whether a jury will hear about EDR data and opinion evidence.

What happens, though, after you have prevailed on the pretrial evidentiary issues, that is, after a court admits the EDR evidence and your testimony? Now that you have convinced a judge that what you have to say is credible enough for the fact-finder to consider, your duties as an expert witness only are beginning . . . and are much more difficult. Those duties are: (1) testifying so that the laypeople of the jury understand you and (2) persuading them that your opinion is the correct one.

Unfortunately, not many resources are available to help you become a better expert witness; you are essentially left to learn “under fire” on the stand. As a prosecutor, I have developed these tips for crash reconstruction experts testifying in our cases which also should be applicable to experts testifying in civil cases. I hope you find them to be helpful.

1. Remember The True Purpose Of Expert Testimony The law school definition of expert testimony is testimony which helps the jurors understand a topic outside the common knowledge of a layperson. The Federal Rules of Evidence (702), as well as the rules of evidence in most states, essentially repeat this definition. When you are preparing for your trial testimony, keep in mind that you will be explaining complicated, technical concepts to people who have no idea about what you are saying. Without revealing any privileged or confidential information, you might want to try explaining one aspect of your testimony to a family member or friend, who does not share your specialized knowledge. If he or she thinks you have presented the evidence and explained the issues clearly, you are on the right track.

2. Use Layperson Language And Everyday Examples/ Analogies Do not be too technical in your choice of words when testifying and lay as minimal a foundation as possible. We all are familiar with the example commonly used to demonstrate this point. Instead of a police officer always referring to a “suspect exiting his vehicle,” he should say, “The defendant opened the door to his car and ran.” The same concern applies in a reconstruction context. For example, where possible, do not testify that with the roadway’s drag coefficient and with the yaw mark’s chord middle ordinate measurement, you were able to determine a vehicle’s speed. (More importantly, do not perform any calculations in front of the jurors, as they will lose interest!)

Simply say that you measured the friction of the roadway and the tire mark and, using an accepted formula based on unchanging physical principles, were able to calculate the vehicle’s speed.

Where EDR evidence is at issue, almost every component of an airbag control module has another use with which jurors more readily can identify. When talking about the OBD II port, for instance, let the jurors know that is where an automotive technician will plug in his diagnostic tool when they take their cars in to be serviced or to receive inspection stickers. The CDR connection hardware essentially is a modem, similar to that found on a home

Collision Magazine

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Becoming an Effective Expert Witness Collision: Volume 7 Issue 1

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William J. Melkonian

computer. The EDR’s flash memory also is in digital cameras. Accelerometers are in place in iPads and iPhones … indeed, any device that has a rotating display.

As an aside, we all are trained not to refer to airbag modules as “Black Boxes,” even though the general public and the media always do. I do not mind using this terminology, because it provides an opening for describing what the box is and, especially, what it is not. (It is not a cockpit voice recorder and is closer to a flight data recorder in an airplane.) These devices also are not new and are reliable; for years, they have been accepted in other modes of transportation, especially in the railroad industry.

3. Do Not Stipulate To Your Training And Experience.

Often attorneys or reconstructionists are so anxious to delve into the substantive trial testimony that they will stipulate to the expert’s qualifications. Other times, they might not think that they have to describe the witness’ qualifications in front of the jury because they already did it before the judge in the pretrial motion. Frequently, the expert’s CV will be offered into evidence without any testimony concerning its contents.

You must understand that a jury’s verdict is a vote on the credibility of the respective experts. Jurors are more likely to accept your conclusions if they know that you have reconstructed a large number of crashes . . . or, identified yaw marks on many occasions . . . or, downloaded and analyzed data from hundreds of boxes. If the case is close, you want the jurors to trust you and feel comfortable with what you have to say.

4. Perform A “Situationally Complete” Reconstruction Related to the point above, even when you believe your analysis of a box download gives a complete picture of how a crash occurred, perform as much of a traditional reconstruction as the circumstances allow – a “situationally complete reconstruction.” Not only will this additional work act as a check on your box analysis, but it also will show the jurors that you did not jump to conclusions after the download. They will appreciate the thoroughness of your work not only in support of your position, but, more importantly, in the quest for the truth.

Conversely, your failure to perform a traditional reconstruction could be fodder for cross-examination, as an attorney might argue that you were being unfair to his client. In Massachusetts, judges may instruct the jurors that if they believe that an investigation was incomplete, they may reject the portions of the investigation that were conducted. Also be aware of the “CSI Effect,” in which jurors, instilled with often unrealistic expectations from the television show, have come to expect that every scientific test possible be conducted in an investigation.

Some Data Limitations in CDR reports explicitly state that box analysis is not a substitute for traditional reconstruction. If you did not perform a traditional reconstruction, be prepared to explain why.

5. Do Not Make “Stuff” Up! When testifying, especially on cross-examination, you might feel tempted to answer a question without fully understanding it or without knowing the answer. Often, this occurs when the opposing attorney “rattles” or angers you. If there is one certainty in situations in which the answer is false, it is that the falsity will be used against you to damage your credibility in the minds of the jurors. Be honest on the stand. Do not be creative; answer the questions as asked. When you do not know the answer, say so (and, in an appropriate situation, that you will find the answer). If the attorney on your side thinks the issue you missed is important enough, he or she will have the opportunity to rehabilitate you. Rehabilitation is much more difficult – if possible at all – when it has been established that you lied under oath or, at the least, made an unwarranted leap of logic in reaching your conclusion.

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Becoming an Effective Expert Witness Collision: Volume 7 Issue 1

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6. Anticipate Challenges From The Opposition You cannot avoid potential challenges to your case by ignoring them. Anticipate what those challenges will be. Think about how you might respond to them. As you handle more crash cases, you will identify the weak spots of your case early in the reconstruction, perhaps even on scene, and will be able to address those issues more promptly.

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7. Run The Most Recent Version Of The Cdr Software Before Trial With each version of the CDR software comes the potential for more of the raw data file to be interpreted.

Unless you are absolutely positive that a newer version of the software will not interpret any more of data, your practice should be to run the most recent version of the software available just before trial. If you do not have the most recent version, find someone who does. You never know what you might find and you just might save yourself some embarrassment if your opposition runs the data with a more current version that you do not even own. Again, your credibility is at stake.

8. Effectively Use Your Trial Exhibits And Physical Evidence While mainly an issue for the attorney who is examining you, too many times a witness is asked to identify a document or a piece of physical evidence, that item is marked as an exhibit, then, placed on the clerk’s bench, without the jurors ever seeing it until they reach the deliberation room. When you are shown an item and testify about its significance, your attorney should ask you to travel the length of the jury box with the evidence in hand so that you can point to the area about which you have testified. Alternatively, the attorney should ask the Court’s permission to “publish” the exhibit to the jury; when this occurs, the testimony stops and does not continue until each juror has had the opportunity to hold and to view the item. Even with all the advances in trial technology, such a wide-screen monitors and computerized projection devices, there simply is no substitute for allowing a juror actually to touch an item.

Become adept at handling exhibits. Be able to effectively describe what is depicted in a particular picture and why that picture is of evidentiary significance. Also, be able to do the same with a piece of physical evidence or with a map. Above all, insist that your attorney effectively uses YOUR exhibits to bolster your credibility and put the case in the best light.

9. Be A Professional Being a professional entails taking pride in your work. Commit yourself to doing the best, most complete reconstruction possible every time. Take continuing education classes. Read publications. Know your case thoroughly. Don’t have so much pride that you can’t ask others for advice or to review your work. Become a resource for information and advice. Watch other trials involving reconstructionists. Make yourself available to your attorney before trial so that you can discuss trial strategy and dealing with weaknesses. Jurors will see your professionalism and your credibility will be enhanced. Above all, enjoy what you do, knowing that it affects lives.

10. Start Strong, End Strong Your testimony should have a strong start when you describe your training and experience; it should end with a strong point. Uncertainty or weakness at the end of your case is not reassuring to the jurors and diminishes your credibility.

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Transcript of the CDR Summit Mock Admissibility Hearing for Trial Preparation

W.R. "Rusty" Haight

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n January 2012, a first-of-its-kind CDR “admissibility hearing” session/ demonstration was conducted at the 2012 CDR User’s Summit in part to create a specially designed and pointed outline for those getting ready to go to an admissibility hearing or trial related to CDR data. Dubbed the “mock admissibility hearing,” the after hours session involving speakers from the Summit and a volunteer expert witness was as well attended as general sessions at the Summit during the day in recognition of its value, relevance and the level of interest of those in attendance.

The “hearing” was recorded by an actual, certified court reporter and the now edited transcript is found on the disk accompanying this issue of Collision. For the hearing, Summit presenter and California Deputy District Attorney Chuck Gillingham was “elevated” to the bench and served as the judge for the session. Chuck was the prosecutor in the first CDR-related case to reach the California Supreme Court; CA v Xinos (like the other presenters listed here, his presentation on that case is found on the disk accompanying this issue of Collision).

ity issues and has written extensively about “Daubert” admissibility issues among other legal topics.

Supporting Tom in the “second chair” for the “defense” was non-lawyer Rusty Haight who organized the session, selected the CDR topics and drew on his experience from more than 30 CDR admissibility hearings to try to try to create issues that would be useful for those reading the transcript.

Good sport and true expert in his own right, Bob Anderson, “volunteered” to be the designated expert witness in the case. While the other participants could draw on their varied experiences, Bob was given specially selected CDR reports specifically chosen to “create” some of the “issues in the underlying case.” Not only were the CDR reports not from the same crash but Bob really had little else to go

on as he took the stand other than his imagination and the “pre-hearing” discussions.

His unenviable job was to draw on his own training and experience and then basically “wing it” where he needed to “fill in the blanks” - which probably turned out to be Massachusetts Deputy District Attorney more often than he would have expected or and Summit presenter Bill Melkonian liked. served as the prosecutor. Bill is an active trainer in Massachusetts for prosecutors On the one hand, Bob wasn’t in the posiand law enforcement particularly on ve- tion he would normally find himself in at hicular crimes cases and was the prosecutor trial: having to work with a limited or limitin MA v Zimmerman, the first CDR-related ing set of actual facts; on the other hand, it appellate case in Massachusetts. In addi- meant he had to creatively think on his feet tion to his presentation from the Summit, as the “hearing” progressed. A lot of credit Bill has authored an article in this issue on goes to Bob for bringing flexibility, creativity and a sense of humor to his time “on “Becoming an Effective Expert Witness.” the stand.” Bob bravely took the “hot seat” Summit presenter, lawyer, author and le- in front of more than 200 of his colleagues gal scholar Thomas Bohan ably took on and peers and came through admirably but the role of defense attorney for the “hear- humbly, adding sufficient levity and imagiing.” Tom is widely recognized throughout nation to his rather unique role.

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B

ut why a “mock admissibility hearing?”

While there are some who have ignorantly suggested that the day of accuracy and reliability hearings / objections or similar issues was over, as this issue of Collision goes to press, there are - in 2012 - CDR-related admissibility hearings scheduled or pending specifically on the issues of accuracy and reliability, “VIN spoofing,” data (CDR file) preservation, “reprogrammed modules,” and even access authority (search warrants) in New Hampshire, Indiana, Georgia, Virginia, Indiana, Texas, Louisiana and elsewhere.

“Admissibility” isn’t limited to the narrow area of the underlying “CDR technology” and its reliability and to believe that is not only to be sadly misinformed but likely ill prepared in the field in a larger sense. As DDA Melkonian laid out in his opening statement as found in the transcript: “...the Court's decision on the arguments yesterday did not make it into the docket, and the government is asking for the Court to make a ruling on EDR technology, in general, and the methodology, in general, as it applies to crash reconstruction at this time. So that the only remaining issues for today's hearing are the applicability of EDR technology to this particular case and the qualifications of our witness Officer Anderson....”

Focusing on “admissibility” only as it relates to the underlying technology is dangerously short sighted. The larger topic of “admissibility” necessarily has to extend beyond the basics.

In short, admissibility of CDR-related data has been offered to the court in three general phases: 1. The qualifications of the expert being offered as it relates to CDR technology in general (in addition, in most cases, to qualification as a crash reconstruction expert) 2. The basic, general acceptance of the technology and rationale behind why the court should follow decisions of the courts in previous cases in acknowledging that the basic “CDR technology” meets applicable threshold for general admission and 3. The reliability of the CDR-related data in the underlying case at hand and it’s applicability and potential to help the “trier of fact” better use that information appropriately in that underlying case.

Fail in one area and you fail on all and there is no admission of the data. History tells us that those with limited meaningful experience may want to believe that challenges to the admission of CDR data is, as one individual simi-

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larly wrote on another subject something along the lines of it being a situation where “...too many (admissibility hearings) have already been (done) and we kind of have that sort of (thing) figured out so we wanted to do something that means something to the (analyst)...” That shallow thinking highlights an utter lack of comprehension of the process and should signal to the client lawyer or prosecutor a dangerous weakness in his proposed expert.

Truth is, as the CDR User’s Summit was being planned, police, private reconstructionists, and lawyers on both sides of the bar sought out transcripts of prior admissibility hearings and went looking for sets of “predicate” questions developed by those who had gone through these types of hearings before almost in direct proportion to the addition of new manufacturers to the CDR Tool and inclusion of more advanced module types and capabilities.

As one lawyer trying to exclude the data argued in a case not long ago, “sure there was that other case here where (OE#1)’s data was admitted, but this case is about (OE#2)’s data in a different type of crash.” In that simple example, lawyers arguing that there are differences between the data sets offered by the different CDR Tool supported OEs, particularly as new ones are added, together with the application to a specific fact set will surely become more commonplace. The underlying “CDR technology” - the airbag control module as a computer, the computer components being commonplace and widely used, reliance on the data generally by various groups, and such - wasn’t in question so much as the application in that case as a function of the differences between the types of data the second OE recorded and the use in a crash with a different fact set.

Moreover, as the number of appellate cases related to CDR technology increases, so do the potential challenges for those using this technology in the field, not only as it relates to the individual’s qualifications but on, in some cases, narrowly focused differences - real or imaginary - between the data sets associated with the various OEs. The Summit organizers and hearing participants tell us that the “mock admissibility hearing” was designed as an opportunity to develop a sample set of questions (and, of course, answers) which, while not necessarily something that would fit every CDR-related case, might instead be a document that would serve as an example of how direct and cross examination on CDR-related topics might be laid out in a hearing or trial. Rather than relying solely on an “actual” transcript and have to wade through the objections and nuances of a specific case, the “hearing” was designed to give general examples of some of the “hot buttons” related to CDR technology from expert qualifications and challenges to CDR-specific challenges and, since

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it wasn’t a “real case,” it would be flexible enough to become a valuable, if general, resource to those in the field.

As 49CFR563 moves closer to implementation and we see the US Senate passing legislation like the “Moving Ahead for Progress in the 21st Century Act (MAP-21)” on to the US House in addition to an expansion of the use of CDR data in insurance settlement decisions, it’s clear that this technology is on the cusp of exponential expansion and, with that, additional attention at trial and elsewhere.

Privacy issues - real or otherwise - lead the headlines today related to the technology where, for example, fully half of “Sec. 31406. Vehicle Event Data Recorders” of “MAP-21” is dedicated to ownership of the data, privacy in the context of access authority or consumer notification. Where “MAP-21” would make 49CFR563 mandatory in 2015 - calling for minimum standards from instrument range to accessability to survivability to data resolution - lawyers seeking to challenge the expert, the technology’s implementation and the accuracy, reliability and/or application of the data to a fact set can be expected to do so ever more aggressively going forward. Only a fool would assume that a lawyer either wanting to use or wanting to exclude CDRrelated data is going to count solely on past admission (or exclusion) as this technology continues to evolve and develop and the remaining issues equally effect admission or exclusion.

Arthur C. Clarke famously wrote “any sufficiently advanced technology is indistinguishable from magic.” To many, the “magic” that is CDR- related technology will play an ever larger role in one side’s effort to convince the average juror - who believes every case, no matter how complex, can be solved in 42minutes - of the strength of their case particularly where it employs a technology the jurors may have, as a function of the media representation, come to see as “magic” or fear or perhaps even resent as an unwelcome intrusion on their lives and privacy.

The CDR User’s Summit mock admissibility hearing wasn’t designed to address the larger reliability of “CDR technology” or the minutiae of a specific case, narrow set of facts or single skill set but rather serve as a general guide toward more efficient qualification of the expert, admissibility of the underlying technology and a basic demonstration of its applicability in a generic case setting.

To that end, the transcript found on the disk accompanying this issue of Collision has been edited from its original format and, the project coordinators readily admit, some liberties were taken with content to improve flow and readability. To facilitate efficient review, the transcript has been reordered to keep topics together. Meaning that, instead of having to read through all the direct examination before

coming to the cross examination, the transcript has been edited such that topical direct and then the associated cross examination have been grouped together.

For example, the first portion of the transcript is dedicated to an opening statement to the court by DDA Melkonian.

That opening statement includes references to a hearing “yesterday” which, of course, never took place but the reference is used as a vehicle for a summation of “why we’re here” and reference to past acceptance of the technology by other courts.

From there, the prosecution attempts to qualify “Officer Anderson” as an expert in CDR technology and, to some degree, crash reconstruction. To his credit one way or the other depending on perspective, Bob Anderson is not and has not been a police officer but he ably and imaginatively handled the direct and then withering cross by counsel for the defense. This section of the transcript includes the direct and cross on the subject of qualifications.

Once qualified, without going into enormous detail, we are lead to believe that Ofc. Anderson did a “sufficient reconstruction of the crash” and then we get into the specifics of CDR data from a GM truck and Ford Crown Vic. End users of the transcript should, again, know that the CDR reports for the GM and Ford vehicles came from vehicles not involved in the same actual crash. The hypothetical scenario developed for the hearing was tied to certain aspects of the data for specific hypothetical issues rather than a logical and ordered reconstruction The transcript has, again, been reordered to put - as best it can be done - the direct and cross on each topic together in the final product.

Some of the issues raised - intentionally - in the “case” included the lack of a search warrant to access the data after the car was impounded, “VIN spoofing” and the loss of the original digital CDR file by the police among others.

While there is, of course, some overlap from one area to another, and that means, in some areas of the transcript, some repetition necessary for clarity, the “hearing team” has tried to maintain the general order of the transcript.

The organizers of the 2012 CDR User’s Summit and participants in the “mock admissibility hearing” hope that, even if only in a small way, the transcript will better prepare someone for an admissibility hearing or trial as it relates to CDR data. The organizers hope that recipients will review the transcript content keeping in mind it is offered as more general than specific and that your own training and experience as well as your own ongoing efforts to stay current with the technology will go a long way toward ensuring CDR is seen as a valuable tool not some sort of strange “magic” as we move forward.

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INFORMATION ANALYSIS ev-i-dence (noun):

FOR THE COLLISION ANALYST

1 a : an outward sign. b : something that furnishes proof.

Jerry S. Ogden, PE, Mathew Martonovich, EI Zachariah Weimer, PE, Katrina M. Kloberdanz, PE.

I

ntroduction While working in the collision analysis field, one is exposed to many different sources of case related evidence and information; some similar in nature across the board, while yet other types of information unique to the particular case being analyzed. To use this case information accurately and correctly, a collision analyst must be aware of the power or the limitations of certain types of information, as well as the potential for analysis pitfalls if some information/misinformation is relied upon too heavily. This article groups case data into two broad, but descriptive categories; objective evidence, and subjective information.

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Understanding the importance, relevance and ev-i-dence (noun): reliability of case data, and properly categoriz1 a : an outward sign. ing the data that is part of an investigation, is b : something that furnishes proof. an important first step to a reliable analysis by a collision analyst. To properly categorize data one ob-jec-tive (adjective): 1 b : of, relating to, or being an object, must truly understand the difference between phenomenon, or condition in the realm information that is “objective” versus “subjecof sensible experience independent of tive” in nature. The further removed an analyst individual thought and perceptible by all becomes from the objective and analytically reobservers : having reality independent of liable information available about an event or the mind. incident, the more doubt can be cast upon the 3 a : expressing or dealing with facts or accuracy of the findings. conditions as perceived without distorbjective Evidence & General Description by personal feelings, prejudices, or tions: interpretations.

1 Webster’s Dictionary defines the words “evidence” and “objective” as follows:

O

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Objective evidence is directly observable, has reproducible properties, by its physical nature is often subject to measurement and/or analysis and is therefore, verifiable and/ or quantifiable, as well as independent of personal feelings, prejudices or interpretations. This is not to say that two reasonableindividuals may not agree to the eventual meaning or value of the objective evidence, which is sometimes the case with technical matters such as those the collision analyst may be called upon to analyze.

indicate just a visual act, but may at times involve some, all or other of the senses of touch, smell or taste.

• For automobile or similar collision events, this may include incident scene measurements, surveys, and detailed notes and observations of debris, gouges, liquids, tire marks, on and off roadway evidence of vehicle travel obtained directly by the collision analyst.

This may also involve the documentation of geometric features of a roadway or incident location, sight lines, traffic control devices, topography, environmental and other conditions that may have influenced or affected the incident.

“" Due to its “physical” nature, collision analysts often label objective information as “physical evidence” of an incident or occurrence.

Due to its “physical” nature, collision analysts often label objective information as “physical evidence” of an incident or occurrence. The following are the means by which objective information, or physical evidence, may be gathered and/or developed by a collision analyst during the investigation and analysis of a collision event or occurrence.

D

irect observation

Direct observation by the collision analyst is selfexplanatory, with the exception that this does not necessarily indicate that the collision analyst must directly observe the collision event, failure or incident in question at the time it is occurring. Instead, “direct observation” identifies the observation and recording of evidence of the aftermath of the event while it is still in part or in whole, present for the collision analyst to observe. It is also important to remember that “observe” does not necessarily

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• The physical inspection, measurement, detailed observation notes and photographic/video documentation of the condition of a motorized or non-motorized vehicle, piece of machinery, etc., post-collision or postevent.

• Observation of the location of a failure, or of a failed mechanical component, structure or device that may include detailed measurements, notes outlining the collision analyst’s observations, and detailed photographic/video recording.

• Sometimes, the direct observation of the lack of evidence of an event, such as the absence of damage to a vehicle or component, or absence of failure to a device or structure claimed to have failed may be just as important to document with measurements, photographs/video and detailed inspection notes as those instances where such evidence is present.

I

ndirect observation

Indirect observation may present itself in the form of photographs, video, detailed measurements or other recordings made by a police officer, another engineer or investigator, claims investigators, or recorded by the involved parties themselves, their agents/representatives, witnesses or bystanders. These are not their personal statements or impressions of the event, but photographs and recordings, or measurements by properly trained individuals. Physical evidence from indirect observation is often relied upon by the collision analyst when it is impractical to observe the condition or aftermath of the event, or due to the passage of time and destruction/degradation of the evidence by the time the collision analyst is involvedmonths or years after the event took place. The reliance upon physical evidence from indirect observation does not necessarily degrade the ability of the collision analyst to accurately analyze the event, but may require additional analysis steps or scientific judgment that would not necessarily be necessary with direct inspection. These types of physical evidence include

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photographs, video, measurements or recordings of the following: • Condition of the incident scene, roadway, failure location, etc. that show the results of the incident, such as post-collision photographs taken by a police or investigative authority, involved party or bystander. Such records are often used for determining the area of impact, vehicle trajectories, vehicle rest locations, secondary impacts, or degree of failure of a component or structure, as well as determining other potentially involved objects in the event.

contained in direct and indirect inspection. Depending upon the type of incident or event under analysis, other types of information that is not necessarily related to the event itself can be reproducible, quantifiable and subject to analysis as it relates to the incident. Such information may be as follows: • Vehicle specification, dimension, structural stiffness, performance and design data, as well as recall, NHTSA defect investigations, etc.

• Scalable diagrams, images, aerial photographs, standard or as-built plans of an incident location.

• Condition of the incident scene, roadway, failed subject, etc., prior to the incident in question. Such records are often used for comparison between the conditions of items/objects/incident scene before and after the event took place.

• Download of airbag control modules (ACM/SDM/ RCM/ORC) and other event recording module (PCM/ROS) on passenger vehicles.

• Download of engine control modules (ECM) or other similar event data recorder (EDR) devices on heavy trucks and/or machinery, buses or locomotive engines.

• Detailed measurements, survey or field sketches of evidence left by the incident upon a roadway or incident location taken by or obtained through a reliable, trained individual such as a police officer or another investigator making direct observations of the incident.

• Diagnostic analysis and/or downloads of vehicles, engines, equipment or mechanical components.

• Direct testing performed by the collision analyst, contracted laboratory or independent organization. This could encompass anything from roadway friction testing or vehicle dynamics, performance and handling analysis, to material strength and failure determination.

• Post collision, or post failure condition of involved vehicles, mechanical components, machinery, devices, structures or other objects of interest. Such records are often utilized to determine degree of deformation, location of a failure, identify failed components, or to identify other features of the object that may be useful during a collision analysis.

• Pre-collision or pre-failure condition of vehicles, mechanical components, machinery, devices, structures or other objects of interest. Again, such records are often utilized to determine degree of deformation, location of a failure, identify failed components, or to identify other features of the object in question that may be useful during a collision analysis.

• Inspection, measurement or documentation of an exemplar vehicle, machinery, mechanical component, device, structure or other object of interest for dimensions, function, operational aspects and/or comparison to the actual object in question.

• Aerial photographs or other rectifiable ground photographs that can be used for forensic mapping and/or diagramming.

O

ther objective information

Objective information, or physical evidence, does not have to take the form of those items

• Standards, known or determined physical properties, historical weather data and other types of information may also become part of the “physical evidence” relied upon by a collision analyst.

As an example, this engineer will often weigh vehicles involved in an incident using our own portable scales or fixed scales at another location, if the vehicles are available for direct inspection. However, since many of the automobile collision cases we are involved in occurred months, or even years before our retention, we may not have the opportunity to weigh the involved vehicles, let alone directly inspect them.

It then becomes necessary to use published data for vehicle weights, or weigh an exemplar vehicle if necessary; still physical evidence since both are presumably reproducible and subject to analysis, if obtained from the proper source and/or methodology. A simple rule is if it can be observed the same at separate times (thus reproducible), can be documented through photographs/video, measured and subject to analytical evaluation or verification, or is a

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standard and/or known physical property, it can be categorized as “physical evidence”.

S

ubjective Information and General Descriptions

Again, Webster’s Dictionary1 defines the words “fact” and “subjective” as follows: fact (noun): 3: the quality of being actual : actuality <a question of fact hinges on evidence> 4 a : something that has actual existence <space exploration is now a fact> b : an actual occurrence <prove the fact of damage> 5 : a piece of information presented as having objective reality Sub-jec-tive (adjective) 1 a : relating to or determined by the mind as the subject of experience <subjective reality> b : characteristic of or belonging to reality as perceived rather than as independent of mind c : relating to or being experience or knowledge as conditioned by personal mental characteristics or states 2 a : arising from conditions within the brain or sense organs and not directly caused by external stimuli <subjective sensations> b : arising out of or identified by means of one's perception of one's own states and processes and not observable by an examiner In the legal community, witness observations, statements, sworn testimony and the like are often referred to as “facts” about an incident or event. From the engineer’s or scientist’s point of view, as well as the definition of the word, this is a very loose interpretation of the word “fact”. It is important that the collision analyst differentiate “fact” from perception, bias, falsehood or fantasy. With respect to the collision analyst and their role in analyzing an incident, event or failure is to keep ever in mind that what is often referred to legally as “fact” is actually “subjective” information, which can only be “factual” if it is proven analytically.

This interpretation of “fact” is what separates an analyst from an advocate; two separate functions and two separate approaches to solving a problem.

I

nformation processing

When dealing with subjective information, it is important to understand its reliability and limitations.

Since subjective evidence comes largely if not almost entirely from people, it is important to understand the limitations of human perception, and retention of information.

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This has been studied intensely by psychologists for many years.2,3,4,5,6 A multitude of studies have tried to quantify the perception and retention of information by human observers, and various factors that can influence the accuracy of recall. This is beyond the scope of this paper, but there are accepted treatises that cover the subject.4,7,8 The primarily sensory information compiled by a witness or an involved party of an automobile collision event or similar event of forensic interest, comes from the individual’s vision. The human eye is limited in its field of vision, anything happening more than 5 degrees from the focal point of the eye requires the eye to adjust, and then fix upon the object to fully interpret what is happening.4 More importantly than even the lack of perception is the lack of retention. Most events of a day are stored in working memory (short term), and very little of working memory is ever transferred into long term memory.4 For an event to be transferred from working memory into long term memory, something out of the ordinary must happen. This would seem to indicate that a witness should have a fairly good recollection of an incident event, but that is not necessarily true. What is true is that a witness may remember something from the event clearly, but not necessarily everything, or even anything useful to the collision investigator. What is most likely to be remembered is whatever captured the greatest attention of the observer, which varies from person-to-person and event-to-event.

This could be something which the observer thought might have put them in danger, or created a startle effect.

When most incidents happen, the event is unexpected, and the duration of the event is very brief, which makes the processes of remembering the important details that much more difficult for even the most keen of observers.

It is no wonder that witnesses to an unexpected incident can all observe the same event, but all have different stories of what took place. These individuals may not necessarily have any motivation to lie or distort what they saw; they just remember the event differently as influenced by their own unique experience, biases and general understanding.

A collision analyst must be extremely careful with the content and accuracy of witness statements, even when a witness confidently believes that they remember what occurred, exactly as it occurred. Oftentimes the few details that a witness truly remembers will be strung together by the mind to make a cohesive story. The mind fills in gaps in memory with what, based upon experiences, seems reasonable and might have happened, but did not necessarily occur as stated. The studies reviewed indicate that the human mind is not very good at remembering details, it remembers the gist of situations much better, and the rest is more or less subconsciously filled in without the individual

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even knowing this is happening. Studies have shown that the more confident a witness is in their statement, does not necessarily correlate with the memory being accurate.2,9 This does not suggest that most witnesses are trying to be deceitful, or trying to mislead, it simply means that human recollection of events tends to be colored by how the mind “reconstructs” the event sequence rather than the actual sequence of the event observed. In this vane, it is easily seen how witnesses’ personal experiences and biases can creep into memory quite unintentionally. Even witnesses who are not trying to mislead may remember something in a light that is favorable to one or more of the individuals involved in the event due to sympathy for one of the parties, biases against another, or a combination of both. There are also witnesses who may try to mislead an investigator intentionally, and one must always be on the lookout for witnesses who have the proverbial “dog in the fight”.

Memory degrades over time and new details can be “added” to the memory of the event which the witness will then begin to accept as “original memories”. Studies have shown that memory is also highly suggestible to influence by others seen in a position of authority or knowledge, and by changing the wording of a question the outcome of the answer can be changed.2,6,10 One must be on guard for suggestive questions and the responses to them. When a witness responds “I don’t remember” to a question, nothing more than “they don’t remember” can be taken away from that answer.

S

ubjective information and opinion should never take precedence over objective evidence

Once the information in a forensic investigation has been properly categorized as “objective” versus “subjective”, the objective evidence should be examined using the appropriate analytical means available to the collision analyst, and within their level of expertise. It is not until after the proper analysis of objective evidence is completed that the collision analyst should examine the accuracy and/or applicability of subjective information. By relying upon the analysis of objective evidence, the collision analyst can reliably determine what subjective evidence is reliable and/or unreliable, thus sub-grouping the subjective information into biased and unbiased categories (Chart 1). Subjective evidence may be tempting to use as the sole basis for forming expert opinions in some situations where it may be favorable to one side or another of an argument. However, this is often not appropriate, as it can over simplify and/ or introduce huge inaccuracies into an investigation and analysis. For instance, vehicle speeds calculated correctly using reliable physical evidence and the appropriate analytical methodology must be given more weight and precedence than the stated speeds of a witness or driver if they differ. Simply put, it is the collision analyst’s duty to trust the proper analysis of objective evidence over subjective information when they differ.

“

Another confounding problem is that most humans are not very good at judging distance and speed, which are some of the most important factors in many analyses.

Studies have shown that people are generally poor at estimating distance in measureable units. People’s ability to judge speed is also generally poor, and can be less reliable than their judgments of distance. As the distance between a witness and an object increases the ability to judge speed decreases exponentially, as the visual clues, namely the image size, decreases. Because most incidents are unexpected, the ability to judge and report speed and/or distance is even less than when actively attempting to remain accurate for a known event. 4,8

The Traffic Crash Reconstruction text from Northwestern University Center for Public Safety11 states it this way:

With that said, what happens when a collision analyst is faced with the situation where there is inadequate or no

…too often the “facts” which have been relied on as the basis for some aspect of collision reconstruction, turn out to be only speculation or even a falsehood… Failure to distinguish fact from opinion is a form of "uncritical thinking". www.collisionpublishing.com

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physical evidence regarding a particular aspect of an event, and all that is available is the subjective information provided by drivers, witnesses or involved parties? Under these conditions, the collision analyst may be unable to formulate any opinions regarding the event, or may be forced to rely solely upon hypotheticals formulated from the available subjective information. One cannot discount subjective evidence simply because it is subjective, but should always recognize that it may not be accurate. With the caveat of recognizing that subjective information has the potential to be variable or inaccurate, it is acceptable in the court systems, and not unusual under these conditions for the collision analyst to be asked to address hypothetical scenarios based upon certain conditions outlined by one party or the other, or agreed upon by all parties in completing the remainder of their analysis. This process is again schematically outlined along the far right side of Chart 1.

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onclusions and Closing Thoughts

The information obtained from statements, depositions and numerous other subjective sources is most often the least “factually reliable” information involved in a collision analysis. Too much reliance upon subjective information can lead the collision analyst into the trap of the “liars contest”, where the collision analyst has to choose one individual’s account of an incident over another’s without proper factual basis. However, this does not take away from the importance of understanding what an individual may have observed or perceived regarding an incident or event. The proper analysis of objective, physical evidence will determine the accuracy of subjective statements and observations, which may be an important finding in a collision analysis. However, the “subjective” should never be used to evaluate the accuracy of the “objective” without a proper scientific basis and supporting physical evidence to the contrary. By carefully following the steps of information evaluation that leads to the proper

Chart 1: Process of using evidence 82

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“ " Collision analysts while functioning as expert witnesses should remain objective, rely upon sound scientific principles and judgment, and most importantly, ensure that all opinions are formulated based upon the most reliable evidence they have available

categorization of case information into “objective” versus “subjective” categories, the collision analyst can avoid setting themselves up with preconceived notions and biased influences that has the potential to negatively affect the accuracy and efficacy of an analysis.

Collision analysts while functioning as expert witnesses should remain objective, rely upon sound scientific principles and judgment, and most importantly, ensure that all opinions are formulated based upon the most reliable evidence they have available, thus producing the most probable scenario for the event or failure. The proper analysis of physical evidence should always trump subjective statements; whether the testimony is given under oath, or not; and whether the individual providing the information “seems” reliable, or not. With these considerations in mind, the evaluation completed by a collision analyst should remain uninfluenced by pre-conceived ideas or biases, and guided by the physical evidence of the incident, thus leading the collision analyst to conclusions that can be stated to within a reasonable degree of probability or certainty.

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eferences

1. Merriam-Webster.com/ dictionary; an Encyclopedia Britannica Company.

2. Smith, et, al., “Eyewitness Accuracy and Confidence; Within-Versus Between-Subjects Correlations”, Journal of Applied Psychology, Vol. 74, No. 2, 1989.

3. Phillips, et, al., “Vision and Visibility in Vehicular Accident Reconstruction”, Paper Number 900369 Society of Automotive Engineers.

4. Olson, Paul L., & Farber, Eugene, “Forensic Aspects of Driver Perception and Response”, Second Edition 2003 Lawyers and Judges Publishing.

5. Cox, et, al., “Improving Witness Contributions to Reconstruction and Animation”, Paper Number 920751 Society of Automotive Engineers.

6. Wells, et al., “Eyewitness Evidence: Improving its Probative Value”, Psychological Science in the Public Interest, Vol. 7, No. 2, 2006 7. Fricke, Lynn B., “Traffic Collision Investigation”, Tenth Edition, 2002 Northwestern University Center of Public Safety.

8. Rast, Peter H., & Stearns, Robert E., “Low-speed Automotive Accidents: Investigation and Documentation”, Second Edition 2003 Lawyers and Judges Publishing.

9. Bregman, Norman J., & McAllister, Hunter A., “Eyewitness Testimony: The Role of Commitment in Increasing Reliability”, Social Psychology Quarterly, Vol. 45, No. 3, 1982.

10. U.S. Department of Justice, “Eyewitness Evidence: A Guide for Law Enforcement”, 1999 U.S. Department of Justice.

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

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CDR DATA FROM MORE THAN ONE CAR? fitting it all together Ray Wangler

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ntroduction As we examine a three vehicle, inline collision, it is discovered that the bookend vehicles – the first and the last – are CDR Tool supported. Working this problem with traditional methodologies using momentum and energy, then comparing that results with the data received from the airbag control modules, one can be confident in their calculations and reconstruction.

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ackground This is a three vehicle, inline collision.

Each of the three vehicles were weighed on certified vehicle scales by officers assigned to commercial vehicle enforcement. Vehicle 3 is a 2009 Dodge Ram 1500 pickup. This vehicle has a total weight of 5750 lbs., including its driver. This is the lead vehicle, and is stopped in the left turn lane waiting on its turn to proceed. This truck is CDR Tool supported. It re-

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cords longitudinal and lateral acceleration data, both positive and negative values on each axis.

This is the first year that we have acceleration information and pre – collision data for this vehicle, and does include non – deployment event data. The pre – crash data is recorded for five seconds, at ten samples per second.

The center vehicle, vehicle two, is a 2003 Hyundai Accent two-door hatchback. This vehicle weighed in at 2650 lbs., including its driver.

There was substantial damage to the front left of this vehicle, and catastrophic damage to the back left of this vehicle. This vehicle is not supported by the Bosch CDR Tool. Based on observations of traffic, this vehicle was stopped seven to ten This vehicle was under – rode by the vehicle be- feet behind the Dodge pickup. hind it, which “scooped” up the rear end of the truck. There were no photographs of the collision damage of vehicle three available. www.collisionpublishing.com

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Figure 1: Front of Hyundai Accent

Figure 2: Rear of Hyundai Accent

The bullet vehicle, vehicle one, is a 1999 Chevrolet S-10 pickup truck. This vehicle is CDR supported, but with an early generation module that captures very basic information and longitudinal delta-V only. Including its driver, this vehicle weighs 3900 lbs. The driver of this vehicle was apparently inattentive.

Both vehicle one and vehicle two were retained by the investigating agency. There were no at – scene photographs taken at the time of the initial investigation and there was no scale diagram measurements taken.

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ethodology

As the best data would be the frontal crash of the Chevrolet pickup, I researched the NHTSA crash test database. I was able locate crash test number 3252 for this vehicle. As the data from the test reviewed, I located and noted the pertinent data needed to calculate the A and B stiffness coefficients:

• Test vehicle weight • Damage width on the test vehicle • Damage profile measurements (net crush) With this data, and using REC-TEC1 collision reconstruction program, I was able to determine the stiffness coefficients for the Chevrolet S-10.

There was no test for a rear impact to a 2003 Hyundai Accent Hatchback.

The next set of data obtained was the crush measurements for the front of Chevrolet and the rear of the Hyundai.

The Tumbas protocol was followed. The damage width was also measured, and there was good correlation between the two vehicles. The Chevrolet was measured first. The baseline for the front of the truck was determined by measuring forward from the rear axle the length of the wheel-

Figure 3: Front of Chevrolet S-10 86

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Figure 4: Front of Chevrolet S-10 www.collisionpublishing.com

Figure 5: Rear of Hyundai Accent

base and the front overhang. The rear axle was used as it had no apparent damage.

The same methodology was used on the Hyundai Accent, except the measurement were from the front axle rearward to where the rear should have been, then the rear overhang was added to that distance.

Using the momentum module in the same reconstruction program, I entered the weights and departure speeds. The impact speed, impulse, and delta-V were essentially the same as the energy solution.

In addition to measuring the damage, the Principle Direction of Force (PDOF) was also determined. The objective of this was to quantify the damage toward a better understanding of delta-V.

As the Chevrolet S-10 is CDR supported, its airbag control module was imaged. The longitudinal delta-V data was at its maximum value at 150 milliseconds into the event, and that was reported to be -30.28 MPH. At 300 milliseconds into the event, the delta-V was reporting at -28.52 MPH. The actual number reported from the energy solution -30.07 MPH.

With the damage profile measurements (crush measurements), weights of each vehicle, and stiffness coefficients, the data was entered into REC-TEC collision reconstruction program for an initial calculation. After the initial run, the problem was run again after the collision forces were balanced, using the Chevrolet S-10 values as a known. Based on this data, closing velocity and delta-V for each of these vehicles was determined. As the Hyundai was presumed stopped, the closing velocity is the same as the impact speed. The closing velocity was 72 MPH. Delta-V for Chevrolet and Hyundai were 30 MPH and 41 MPH, respectively.

Having the excellent correlation between the momentum, energy, and the reported delta-V from the airbag control module, most reconstruction experts would have been content with the results. It was approximately six weeks before the owner of the front vehicle, the Dodge Ram 1500, came to give a statement to the investigating detective on the collision. It was only after realizing that the Dodge’s Occupant Restraint Controller module captured both positive and negative acceleration values that the decision to image this module was made. This module also captured pre-collision data for five seconds at ten samples per second.

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Figure 6: Delta-v chart from Chevrolet S-10

The CDR report from the Dodge gave several important pieces of information, namely that the vehicle was stopped or just barely moving for a very brief period of time. It also gave the longitudinal and lateral acceleration data. From this, the delta-V of the Dodge pickup was calculated. In this case, the resultant delta-V was 12.54 MPH. The pre-collision data was further analyzed, specifically the data from the wheel speed sensors. At time stamp -0.1 second, the front right and left wheel speed in RPM was noted. The rear wheel speed was not factored in, as the rear of the vehicle was lifted by the front of the Hyundai, as evidenced by the damage (or lack thereof ) to the front bumper. The speed from the two front wheels, averaged together, yielded a speed at -0.1 second of 12.36 MPH. The discrepancy here is minute, and may be attributed to the actual wheel rolling radius.

Using the post – impact speed of the second collision, being that of the collision between the Hyundai and the Dodge, as a common speed for both vehicles, the calculated impact speed of the Hyundai into the Dodge is 39.74

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MPH. The post – impact of the Hyundai coming out of the first crash calculated to be 42.27 MPH. An attempt was made to reconcile the difference between the two impact speeds. Using an average roadway coefficient of friction of 0.7, and allowing 40 percent braking efficiency on the Hyundai, and allowing 7 feet of travel distance between the Hyundai and Dodge, a post – impact speed on the Hyundai of 41.56 MPH was calculated.

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onclusions:

The main conclusions reached in this study are:

• Event Data Recorder (EDR) has been shown to be reliable when compared to an energy solution and a momentum solution.

• The impact speed of vehicle 2 coming out of the first collision and going into the second collision are very close, with a difference of 1.82 MPH. This difference could be attributable to the Hyundai’s braking efficiency, variation in the roadway frictional value, or the exact distance between the Hyundai and Dodge truck.

• The methods used were applied independently with the available case facts and data that were developed.

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Figure 6: Last four samples from Dodge Ram CDR Report

• The use of multiple methodologies served to verify or corroborate each other, and the methods used are those that are generally accepted in the collision reconstruction community.

• The developed data and that recovered using the CDR program complimented each other and gave what might be considered a “more complete picture” of the collision.

• Although there was very limited data from Chevrolet S-10 pickup’s airbag control module, and was basically limited to delta-V data only, this analysis shows that delta-V “alone” is obviously a very useful data element worthy of application in the analysis.

In the end, the case studied lent itself to using CDR data from one of the involved vehicles together with and compared with data developed during a normal situationally complete reconstruction showed good correlation, and when compared with data coming from the other end of the crash, verified the calculated collision speeds in this study.

Footnote: 1REC-TEC is the intellectual property of George Bonnet, J.D., and is used and referenced by permission.

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Measure and Diagram Scenes and Vehicles from Photographs

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· Quickly clear and measure scenes with your camera (ground-based or optionally aerial photography) · Measure crush using photos from your camera, or unknown cameras · Extract key evidence (distance, position, angles) from 3rd party photos · Model vehicles in 3D using a camera · Photo-based measurement for law enforcement, engineers, and reconstructionists since 1993 · Self-train with detailed video tutorials, attend public training held several times each year, or book a private course · Floating concurrent network licenses available for multiple users PhotoModeler and PhotoModeler Scanner share these common features: · Fully integrated support for digital and film cameras · Use 3rd party photos and unknown cameras with InverseCamera · On-photo dimension lines for basic diagrams · Export to other CAD, diagramming, or visualization packages (14 formats) · Sophisticated integration of control points from total stations or other sources · Map scenes over large distances using Project Merge · Extract photographic texture for realistic 3D models · Support for single photograph projects - great for forensics · Ortho-photo generation for that “birds-eye” view of your scene · Use the Surface Draw function to add 3D points, lines and curves traced on a single photograph

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Close-Range Photogrammetry for Sightline Obstruction Determination Lee DeChant and Gary Cooper

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ntroduction

In accidents involving personal injury, photography can often play a critical role in proving or disproving statements made in litigation regarding a vehicle incident. Close-Range Photogrammetry (CRP) techniques are used in accident scene reconstruction to derive accurate three-dimensional (3D) measurements from photos taken at the time of the event. CRP is a proven measurement technique that can extract 3D information from two-dimensional photographic images. This article reports on the investigation of a crash where a car was

turning left from a private road on to a U.S. highway and was struck on the driver’s side by an on-coming SUV. The plaintiff’s complaint in ensuing litigation centered upon the fact that a snow pile adjacent to the private road gave rise to a dangerous sightline obstruction. The snow pile, shown in Figures 1 and 2, allegedly prevented the car’s driver from seeing the oncoming SUV that struck her vehicle as it entered the highway. CRP was accomplished using numerous printed photographs acquired by the police shortly after the incident.

The 3D photogrammetric measurement and modeling of the snow pile, along with other pertinent evidence, proved to be a key factor in the jury’s 13-0 decision in favor of the defense, and hence no fault was attributed to the parties being sued.

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he Incident

The crash involved a 1989 Ford Bronco II (SUV) and a 1996 Chevrolet Cavalier (car) that collided on February 23rd, 2003. The SUV was traveling eastbound on U.S. 40 in Belmont County, Ohio, approaching a private road on the south side of the highway. The car was exiting the private road to proceed westbound on U.S. 40.

Importantly, there was a pile of snow at the southwest corner of the intersection of the private road and U.S. 40. This had been placed there by the construction company that owned the private road. The car pulled out in front of the SUV and the two vehicles collided. The car driver was rendered a quadriplegic as a result of the crash, and she subsequently sued both the SUV driver and the construction company.

The issues addressed within the crash reconstruction concerned the velocity of the SUV, the

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time/distance relationship between the SUV and car, and whether the snow pile restricted the car driver’s sightline to the approaching SUV. The car was equipped with an air bag control module (ACM) that recorded the longitudinal change in velocity experienced by the car during the collision. The data from the ACM and momentum were used to estimate the velocities of the car and SUV at impact.

The SUV driver applied the brakes and the SUV left skid marks before impact. Both the length of these and the impact velocity were calculated and used to determine the initial velocity of the SUV.

“"

The legal action taken by the plaintiff was also directed at the driver of the SUV. However, after calculations by experts for the plaintiff and defense indicated that he was most likely not exceeding the speed limit at the time his vehicle began to skid, he received a summary decision from the judge and was dismissed from the case.

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he Snow Pile Question

Experts for the defense used the same four police photographs to carry out a more rigorous, truly multi-image photogrammetric analysis. The photographs provided a satisfactory diversity of viewing directions and photo-overlap of the snow pile to support image-based 3D measurement. The scene was also surveyed several months after the incident, after the snow had melted, using a total station surveying instrument. The purpose was to provide reference points of known position, these being referred to as control points. The control points were subsequently used within the iWitness™ close-range photogrammetry system (www.iwitnessphoto.com) to accurately determine the po-

The dimensions of the snow pile were not recorded by the police during their investigation. However, the police did position a police car at various locations on the private road and checked the sightline west along U.S. 40 from each location. The police concluded that the snow pile was not a factor in the incident.

It was a straightforward matter to validate the accuracy of the multiimage photogrammetric technique through a comparison with the measurements performed using the total station.

The plaintiff claimed the snow pile restricted the sightline of the car driver and thus posed an extremely hazardous situation, which was the principal cause of the crash. In deposition, it was further claimed by the plaintiff that ‘photogrammetry’ had been employed to accurately measure the height of the snow pile. The method used was to draw three (yellow) horizontal lines across two police photographs that were recorded shortly after the incident. These can be seen in Figures 1 and 2.

The plaintiff’s team was not able to provide any mathematical basis or scientific methodology to support the determination of 4.1 feet for the height of the snow crest. The basis for this arguably unorthodox, so-called ‘photogrammetry’ determination appeared to use the height of a Toyota Tercel as a reference to determine snow pile height. The differential height between the snow pile and the car was measured with respect to the three horizontal lines, independently on the two photographs. There was no evidence provided that the Tercel’s height measurement was even in the same vertical plane as that of the snow pile measurement, thus neither providing correction for image perspective and image scale difference, nor accounting for camera tilt.

sition and precise aiming direction of each of the four photographs. This process takes account of camera calibration and it provided a basis for determination of 3D measurement of points of interest on and adjacent to the snow pile via photogrammetric triangulation. Figure 3 shows the array of measured 3D feature points within iWitness.

The individual processes used to enable scanned photographs to be digitally processed within the iWitness software system were Focal Length From One Image (FOOM), which determines camera position and orientation, and Zaraf, which assigns the necessary metric information to the scanned images to support photogrammetric analysis.

The highlighted yellow 3D points in Figure 3 were the basis of the determination of six different locations for snow

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Figure 1: Horizontal lines across police photograph that were recorded shortly after the incident

Figure 2: Horizontal lines across police photograph that were recorded shortly after the incident

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Figure 3: Array of measured 3D feature points within iWitness

Figure 4: iWitness user interface and the four specific police photos used for the photogrammetric measurement

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pile height, later used in a CAD program for further sightline obstruction analysis.

Figure 4 illustrates the iWitness user interface and the four specific police photos used for the photogrammetric measurement.

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hotogrammetric measurement accuracy

In contrast to the serious doubts associated with the validity of the measurement methodology presented by the plaintiff, it was a straightforward matter to validate the accuracy of the multi-image photogrammetric technique through a comparison with the measurements performed using the total station. A number of features were measured by both iWitness and the total station and the agreement between the two approaches indicated a photogrammetric accuracy of generally better than 1 inch, as indicated by the check-distance results in Figure 5.

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ightline Analysis

The 3D XYZ coordinate measurements from iWitness were imported into two different CAD programs, namely Rhinoceros™ and Crash Zone™, in order to map the scene, as illustrated by the diagrams of the

A snapshot of Photogrammetry Close-Range Photogrammetry is a non-contact image-based technique for generating 3D measurements of objects and scenes from multiple overlapping photographs. It has traditionally been applied in topographic mapping, but through recent technological developments in digital cameras and personal computers, photogrammetry is now widely employed as a fast, accurate and easy to apply technique for measurement tasks including 3D modeling for heritage recording, engineering metrology and forensic measurements such as accident reconstruction.

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collision in Figures 7 and 8. The HVE Highway Safety Research software from Engineering Dynamics Corporation was used to carry out the sightline analysis. The car was placed on the private road near U.S. 40 at various virtual locations within the HVE environment. The extent to which the snow pile affected the car driver’s line of sight to the approaching SUV was then visually analyzed.

Figures 9 and 10 illustrate simulated views generated within the HVE system using the photogrammetric measurements of the snow pile. Along with the snow pile and roadway, other features of interest such as signage, road markings and mailboxes were also shown in the virtual scene.

It was determined through HVE that with the car positioned two feet back from the fogline of U.S. 40 (Figure 8), the driver could have first seen the SUV when it was 400 feet from the intersection (Figure 9). The view of the SUV would have been significantly better at a distance of 250 feet from the intersection (Figure 10). With the front of the car at the fogline, the SUV could have first been seen by the car driver when it was 350 feet from the intersection.

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onclusion

The photogrammetric measurement work and subsequent sightline analysis formed the foundation upon which the defense built its successful case, with the testimony on the crash reconstruction being compelling to the point where it was not challenged in any way during the trial.

This example of photogrammetric measurement to support forensic analysis highlights the general need for a 3D modeling approach. The iWitness photogrammetry measurements, made from four images not specifically recorded for the purpose of scene reconstruction, clearly highlights the flexibility and accuracy of the multi-image photogrammetry approach. Both the height and feature point measurements needed for the sightline analysis and visualization within a 3D analysis program, cannot be determined either rigorously or accurately from a single image, or from two images independently as was attempted in the work carried out for the plaintiff. On the other hand the iWitness photogrammetry approach performed for the defense can employ any number of images in order to provide a comprehensive 3D mapping of the accident scene.

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Figure 5: Check-distance results

Figure 6: iWitness photogrammetry interface www.collisionpublishing.com

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Figure 7: Diagram of the collision

Figure 8: Diagrams of the collision

Figure 9: SUV when it was 400 feet from the intersection

Figure 10: SUV when it was 250 feet from the intersection

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& Affordable and exceptionally easy-to-use photogrammetric systems for fast and accurate 3D measurement and modeling from images recorded with consumer-grade and professional cameras • Very intuitive and simple to operate • Automatic recognition of the camera • Fully automatic camera calibration, as well as on-the-job self-calibration • Calibrated measurement from zoomed images (iWitnessPRO) • Fully automatic 3D measurement of targeted objects (iWitnessPRO) • Advanced facet and free-form polyline curve generation • All photogrammetric orientation processes are initiated automatically • Robust and reliable error detection through on-line data processing • Flexible generation of photo-textured 3D models • Automatic (iWitnessPRO) and auto-assisted image marking and referencing lead to high accuracy XYZ coordinates • Efficient and fast ‘stitching’ of multiple overlapping networks • Export of 3D measurement data for display in CAD systems and Google Earth • Works with zoomed images and also images from unknown cameras

iWitness™ & iWitnessPRO™ are widely used in engineering, architecture, heritage recording, animation and modeling, and especially in accident reconstruction and forensics.

Versions 2 of iWitness and iWitnessPRO are now available, with full backwards and forwards compatibility, free patch upgrades and no annual maintenance fees. ©2012 DCS Inc. All rights reserved.

Contact DeChant Consulting Services for your complete turnkey photogrammetric solutions to image-based measurement.

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Toyota, Lexus, and Scion CDR COVERAGE Kent E. Boots

FactualDiagrams.com

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osch released Version 4.0 of the Crash Data Retrieval (CDR) Tool software June 24th, 2011. This release of the software gave us access to Lexus vehicles from 2006-2012, Scion vehicles from 2006-2011, and Toyota vehicles from 2006-2011. Not all models in the respective years were covered. Version 4.2 of the software was released on November 16, 2011. This release added coverage for select 2012 model year Lexus, Scion, and Toyota vehicles. Version 4.3 of the software was released on December 22, 2011. This release included

significant new coverage for 2012 and back model year Toyota, Lexus, and Scion vehicles. This release gave us access to Lexus vehicles back to 2001, Scion vehicles back to 2004, and Toyota vehicles back to 2002. It also “filled in” and provided coverage for vehicles not previously covered in version 4.0. www.collisionpublishing.com

When Version 4.0 was released North American sales for Toyota, Lexus, and Scion from 2005 – 2011 was nearly 14.5 million vehicles. Toyota estimates that 80% of all Toyota, Lexus, and Scion vehicles sold in North American in the last 20 years are still on the road.

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The data recording specifications of Toyota’s airbag electronic control units (ECU) are divided into the following three generations and ultimately six categories: • Generation one (Gen 1) - 00EDR & 02EDR: Which covers approx. calendar years of 2000 through 2010.

• Generation two (Gen 2) - 04EDR & 06EDR: Which covers approx. calendar years of mid 2004 through 2010.

• Generation three (Gen 3) – 10EDR & 12EDR: Which covers approx. calendar years of 2012 on.

We will not see 10EDR modules in the North American market. The 12EDR modules are designed to be compliant with the requirements of NHTSA’s 49CFR Part 563 rule.

The airbag ECU has recording capabilities for all or some of the following accident types: frontal crash, rear crash, side crash, and rollover events. The airbag ECU may not record side crash and/or rollover events depending on the module. This is indicated by vehicle coverage notes in the CDR help file. Notes 5 and 6 indicate the vehicle does not record rollover data and side crash data respectively. Notes 8 and 9 indicate the vehicle may not record rollover data and side crash data respectively. The Toyota airbag ECU will not only “wake-up” on rear impacts but it will capture crash and pre-crash data related to the event.

Each module is capable of storing two of each type of event it captures. So for modules capable of recording rollover data and side crash data they can store a total of six events; two front/rear, two side, and two rollover.

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hat about the modules?

Toyota refers to the airbag ECU as the Center Airbag Sensor Assembly. There are four different manufacturers of modules; Toyota, Denso, TRW, and Fujitsu Ten. At least one of the Denso modules has a separate yellow and white label. The rest of the modules have a combination white/yellow label or white/orange label.

The yellow/orange labels all say “caution SRS airbag” on them in numerous languages including English. The white labels either contain the words “airbag” or “airbag sensor.”

The White labels also contain the part number and serial number. The Toyota module has a 7 digit serial number located next to the bar code. The Denso module has an 11 digit serial number located either above or below the bar code. The TRW module has a 10 digit serial number located under the bar code. The Fujitsu Ten module has a 10 digit serial number located next to the bar code.

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The modules are typically located along the centerline of the vehicle. The Bosch CDR help file lists the following locations: • Lexus • Under center console (IS-all, LFA, LS: 2006+) • Under center stack (LS:2001-05, all others) • One or the other (GS) • Scion • Under center console (iQ) • Under center stack (all others) • Toyota • Under center console (RAV4) • Under center stack (all others) Further research of service information shows the modules listed as “under center console” either being near the rear portion of the center console under the armrest or under the front portion under the cup holder/change area. Those modules listed as “under center stack” were located directly forward of the leading edge of the instrument panel. In some cases there were removable panels to access the modules.

The modules have the typical 3-tab configuration and are mounted with 12 mm bolts. Most modules connect to the vehicle using a single plug that connects to three separate receptacles on the module. The connector is removed from the module by pulling a lever away from the module. The only exception to this is those vehicles that call for cable number F00K-108-617. These modules have three separate plugs that are removed individually from the module.

When connecting directly to the module to image data, the CDR cable is plugged into the center receptacle.

Toyota airbag control modules are typically not fused on a separate circuit. They are fused on the same circuit as the ignition; IGN. Most owners’ manuals specifically list SRS airbag system in addition to the other circuits which the fuse protects. There have been a few exceptions to this. The 2007 Toyota Sequoia, for example, does not list the airbag control module separately. Electrical schematics show the ACM being protected on the IGN1 fuse which is listed in the owner’s manual as “Charging System.” The 2007 Toyota Corolla lists “SRS Airbag System” in the owner’s manual on the AM2 fuse; AM referring to air mass. The same fuse also protects the fuel injection system. The 2012

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Toyota Camry lists “A/B” as a fuse protecting the occupant classification system, and SRS airbag system.

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he Bosch CDR Report

The Bosch CDR report layout for Toyotas is similar to what we have seen with other manufacturers.

On the first page of the report, the CDR File Information block will list the events recovered as Front/Rear, Side, or Roll followed by a number in parenthesis to indicate the total number of that type of event captured as shown in Figure 1. There are over two pages of data limitations providing CDR Record Information, General Information, and Data Definitions.

The first page of the report following the data limitations is the System Status at Time of Retrieval as shown in Figure 2. This block shows the ECU generation for the module.

It also gives the Freeze Signal and Freeze Signal Factor information. If the airbags did not deploy or the pretensioners did not fire during an event that meets a specified recording threshold, this is considered a non-deployment event. The Freeze Signal indicates a state in which the nonvolatile memory cannot be overwritten or deleted by the airbag ECU. If the Freeze Signal value is OFF, then the

Freeze Signal Factor value would be None. This would indicate an event was not locked and is the equivalent of a non-deployment event. Data from a Non-Deployment Event can be overwritten by a succeeding event that meets the specified recording threshold.

If the airbag(s) deployed or the pretensioners fired, this is called a deployment event. Deployment event data cannot be overwritten or deleted by the airbag ECU following that event. If the Freeze Signal value is ON, then the Freeze Signal Factor value would state what type of deployment there was such as Front Airbag Deployment. This would indicate an event was locked and subsequent events would not be recorded.

This section also includes the Time from Previous Pre Crash TRG in milliseconds. This time value indicates the time between the establishment of an event's pre-crash recording trigger to the establishment of a more recent event's pre-crash recording trigger. The upper limit for the recorded value is 16,381 milliseconds; a little more than 16 seconds. Looking at this value may help determine whether two events are related or not.

Figure 1: CDR File Information from Bosch CDR Report

Figure 2: System Status at Time of Retrieval from Bosch CDR Report

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Collision Magazine 103

The next section on the same page is Event Record Summary at Retrieval as shown in Figure 3. This table lists the total number of events captured by the airbag ECU. They are titled Most Recent Event, 1st Prior Event, 2nd Prior Event, etc.. There is also a TRG count which indicates a calculated value of the number of times recording triggers have been established for all crash types. The chronological order in which events occurred can be verified from the TRG Count. The smaller the value, the older the data. In the example shown in Figure 3 there are three events corresponding to TRG counts 1, 2, and 3. However, the events may not be contiguous due to non-deployment events, and there could be gaps in the Events Recorded such as Most Recent Event, 2nd Prior Event, 4th Prior Event. It is also possible to see TRG Count values higher than six.

In other words the airbag ECU could have experienced a total of 10 Front/Rear Crash events over its lifetime, but of course will only record two of them. The TRG Count is a lifetime counter for the module with an upper limit for the recorded value of either 254 or 65,533.

The subsequent pages of the CDR report will give the System Status, Crash Pulse, DTCs present, and pre-crash data for each event. They will be labeled in accordance with the Events Recorded listed in the Event Record Summary table. The System Status at Event, shown in Figure 4, provides valuable information about the system at the time of the event. You will see references to Linked Pre-Crash Page and the values will be either a zero or a one. These refer to a memory location in the airbag ECU.

Reported time values for Time to Deployment Command, are indicative of a deployment. Had this event been a non-deployment, the reported value would be Not Commanded. The Event Severity Status is a data element used for deploying multi-stage airbags, and is typically reported as a 1, 2, or 3 corresponding to internal indications of “low”, “medium”, or “high” (severity) respectively for frontal events only. If the vehicle has a single-stage airbag, the recorded value will always be “Level 3” if airbags were deployed. This item is not captured and reported in the 12EDR ECU generation because the airbag ECU uses the time from first-stage deployment to second-stage deployment.

The crash pulse will be longitudinal for Front/Rear events as shown in Figure 5. The maximum longitudinal Delta-V will be reported in MPH and km/h. The crash pulse will be displayed in both a graph, speed with respect to time, and tabular format. In the example shown there is a vertical line labeled D/P Airbag Front Pretensioners. This line indicates wake-up and may not appear in all reports. In any case, the first sample is reported at algorithm enable (AE) or wake-up and goes forward in time from there. On this module (04EDR) the resolution is every 10ms and there are a total of 20 samples for Front/Rear events.

The lateral (side) crash pulse will list the maximum lateral Delta-V at the B-Pillar Sensor and C-Pillar Sensor in both MPH and km/h (not shown). The crash pulse will be displayed in both a graph, speed versus time, and tabular

Figure 3: Event Record Summary at Retrieval from Bosch CDR Report

Figure 4: System Status at Event (1st Prior, TRG 2) from Bosch CDR Report

104 Collision Magazine

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format. There will be three graphs; one for the airbag ECU sensor, one for the B-Pillar Sensor, and one for the C-Pillar Sensor.

On this module (04EDR) the resolution is every 4ms and there are a total of 25 samples for Side events; 6 before trigger/19 after trigger. There is not a sample taken at the trigger. The time series data for side crashes may have 24 or 25 sampling points. In the case where there are 24 samples there will be 6 before trigger/18 after trigger.

Pay special attention to the System Status at Event for Side Events. There will be an item titled Recorded Side. It will either be reported as Driver’s Side or Passenger Side. The pillar and door sensors will report the Delta-V as a positive value when the direction of the force is from the outside to the inside of the vehicle regardless of which side of the vehicle is “involved” in the crash. It is possible to see a Delta-V reported by one of the pillar sensors as a negative value if there was some rotation around the center of mass of the vehicle. The airbag ECU will conform to SAE J1733 and J211 and report a positive value when the direction of the force is coming from the left side (toward the right side) and a negative value when the direction of the force is coming from the right side (toward the left side).

Figure 5: Longitudinal Crash Pulse Graph (1st Prior, TRG 2) from Bosch CDR Report

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Collision Magazine 105

The rollover crash pulse will list the roll angle peak in degrees with the resolution being 0.1 degrees (not shown).

The peak degrees listed in the table may not always match the peak value within the roll angle sampling points due to differences in the data calculation method. There will be two graphs; one showing the roll angle in degrees with respect to time. The second will be the lateral acceleration as observed by the airbag ECU sensor. It is reported in m/ sec2 with respect to time. On this module (04EDR) the resolution is every 8ms and there are a total of 20 samples for Rollover events; 4 before trigger/16 after trigger.

P

re-Crash Data Parameters

driver and passenger are reported and monitored using hall effect switches. The occupancy status for the passenger is monitored and depending on the type of occupant sensor installed in the vehicle, one of the following three recording formats will be utilized.

• Occupied / Not Occupied • Adult / Child / Not Occupied (Child is less than 36 kg (79.37 lbs)) • AM50 (adult male 50th %, ≈ 170 lbs) / AF05 (adult female 5th% ≈ 110lbs) / Child / Not Occupied

The page following the crash pulse table for each event will list the diagnostic trouble codes (DTCs) present, and pre-crash data for the event as shown in Figure 6. The DTCs are information about faults that existed when a recording trigger was established. It is possible for DTCs to have been set as a result of some component damage during a crash. The airbag ECU only records diagnostic information related to the airbag system. It does not record diagnostic information related to other vehicle systems.

Toyota accomplishes occupant detection with either a bladder sensor or a strain gauges combined with a belt tension sensor. The driver’s seat position is monitored with a hall effect switch mounted to the seat rail. It reports either forward or rearward of the switch point which is located at approximately the forward 1/3 point of the seat track.

The Bosch CDR report will list "R,"(Reverse), "N" (Neutral), or "P" (Park), and "Drive" for the shift position. The "Drive" setting for the "Shift Position" value indicates the shift position state is other than reverse, neutral, or park.

So this could be drive, 1st, low, etc.

The pre-crash data timing is based upon the time from the final sampling point of the precrash data to the establishment of the recording trigger

There are a total of six pre-crash time samples reported approximately every one second. 12EDR ECU generations will capture pre-crash data every 0.5 seconds. The pre crash data parameters captured are vehicle speed, brake switch, accelerator rate, and engine RPM. Due to different refresh rates within the vehicle's electronics, a particular data parameter sample recorded may not be synchronous to other pre-crash data parameters.

The pre-crash data timing is based upon the time from the final sampling point of the pre-crash data to the establishment of the recording trigger (TRG). This time is reported in the first pre-crash data table; 900ms in this case. The resolution of this time is 100 ms, and the value is rounded down and recorded. The buckle switch for both

Toyota uses the term "accelerator rate" to indicate the amount the accelerator pedal is being depressed. There are two accelerator pedal position sensors (APPS) that monitor system voltage. Of the two sensors, APPS 1 is the one that is monitored by the airbag ECU. The voltage increases as the driver depresses the accelerator. Toyota re-

“ 106 Collision Magazine

The vehicle speed has an upper limit of either 122 km/h (75.8 mph) or 126 km/h (78.3 mph) depending on the module type. The data limitations will specify which upper limit is in place. The resolution for the speed is 2 km/h (1.2 mph) and the value is rounded down. The refresh rate for the speed is every 500ms. As would be expected the accuracy of the vehicle speed value can be affected by various factors including, but not limited to changes in the tire's rolling radius, wheel lock, and wheel slip.

The "brake switch" indicates the open/closed state of the brake switch circuit. It is continuously monitored and its status is reported at the event.

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ports the voltage ranges from 0.8 volts (fully released) to 3.5 volts (fully applied). The CDR report will range from 0.78 volts to 3.59 volts due to rounding. The resolution for the voltage is 0.039 volts. The refresh rate for the accelerator rate is every 512ms. The CDR report will display one of three recording specifications: • Percentage of accelerator pedal depressed; recorded as 0-100(%) • Output voltage of accelerator pedal module; recorded as 0-5(V) • Level which is the degree of depression; recorded as off, middle, or full Something to remember is that with a throttle-by-wire system a vehicle being operated with the cruise control on will report no accelerator pedal application.

A review of Toyota read-out tool files (ROT) on the NHTSA website shows APPS1 voltages reported as high as 3.71 volts. It will likely take some more testing in this area and looking at scan tool data from various vehicle platforms

and airbag ECU generations to determine with more certainty the accuracy and resolution of the reported acceleration rate values.

The engine RPM ranges from 400 to 5200 RPM, and has a refresh rate of every 24ms. The resolution for the engine RPM is 400 RPM, and is rounded down. For example, if the actual engine RPM is 2350 RPM it will be reported as 2000 RPM.

F

inal Thoughts

Toyota, Lexus, and Scion CDR reports may indicate conditions or characteristics of the data that may be, on the surface, confusing or require a more complete analysis by other means. The data may be affected by conditions or vehicle characteristics described in the Data Limitations section of the report or in other related reference material.

As is the case with data from other vehicle manufacturers a situationally complete analysis of the crash or event under study should be undertaken to fully evaluate the meaning and usefulness of Toyota, Lexus, and Scion crash data obtained with the Bosch CDR Tool.

Figure 6: Data from Bosch CDR Report

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Collision Magazine 107

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

                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 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“ 2 in - 3 out ” Use our FX3 realmotion 3d to instantly animate your reconstruction results on your 3D scene.

  



Use our SmAc FX to simulate or predict collision results.

Present powerful time distance reports and persuasive graphical output.

Overwhelm them!

All FX3 output is detailed and court acceptable.

The International Compendium for Crash Research

Two-Dimensional Analysis of EDR Information

2007 CDR User’s Conference CD

Analysis of Collisions:

Conservation of Linear Momentum

Presentations & Data

The Accuracy of GM EDRs in NHTSA Crash Tests

Reconstruction & Analysis of Steering-induced, On-road, Untripped SUV Rollover

Use our crash FX momentum tool right on the diagram to generate impact speeds.

Use our crashmath application with 720+ reconstruction formulae to get all the answers.

One integrated workspace.

2006 CDR User’s Conference

www.CollisionPublishing.com

You never leave one application - FX3

From The Scene

1.888.828.0383

www.sokkia.com

Comparing Different Sources of CDR Data To “Real World” Crashes

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In this issue

“Multiple Case Studies” Plus . . .

For a full reconstruction of this 46 second chase sequence, visit www.edccorp.com/bullitt

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

The Accuracy of Speed Recorded by a Ford PCM

Questions

and the Effects of Brake, Yaw and Other Factors

Speed on Taylor St.

Seeing Across the Years

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Using Data from a DriveCam Video Event Recorder to Reconstruct a Hard Braking Event Nathan A. Rose William T.C. Neale Neal R. Carter

110 Collision Magazine

www.collisionpublishing.com

I

ntroduction This article examines the validity of using data recorded by a DriveCam in-vehicle event-triggered video and data recorder to reconstruct a hard braking event. A DriveCam unit is an aftermarket system that contains accelerometers to measure both longitudinal and lateral accelerations and two cameras that record video. A DriveCam unit is mounted to a vehicle’s windshield in the area of the rear view mirror (Figure 1) and one of its cameras looks forward out the windshield and the other looks rearward at the vehicle occupants. The DriveCam system constantly monitors the acceleration (and sometimes speed) of the vehicle and also constantly records video. However, this data is only stored when the accelerometer in the unit measures an acceleration that exceeds a preset threshold. When this occurs, the DriveCam system records an event and uploads data related to the incident to DriveCam’s data center. This data can then be accessed and analyzed to evaluate the performance of the driver and to determine the sequence of events that led to the acceleration threshold being exceeded.

www.collisionpublishing.com

Collision Magazine 111

Figure 1: Schematic Depicting a Typical DriveCam Unit

DriveCam systems are commonly used by companies attempting to monitor and improve the performance of drivers in their fleets. When a vehicle from a commercial fleet is involved in a crash, and that vehicle also happens to contain a DriveCam unit, the data from the DriveCam system will often make its way into the hands of an accident reconstructionist who will use it in the course of analyzing the crash. The question, thus, arises to what degree this data can be relied upon for reconstructing a crash.

The research reported in this article sought to answer this question, in part, by examining the validity of using DriveCam data to reconstruct a hard braking event. The authors conducted acceleration and hard braking tests with

two DriveCam equipped vehicles – a 1994 Toyota Land Cruiser and a 2002 Chevrolet Cavalier. For each test, the vehicle began at rest. The driver accelerated the vehicle at a relatively aggressive rate for a predetermined time and then braked aggressively. The braking level in these tests was sufficient to initiate the recording of an event by the DriveCam system. Following the tests, we obtained the event records for each test from DriveCam. The test vehicles were also equipped with another data acquisition system (a VBOX system from Racelogic) that would report the position, speed and acceleration of the vehicle in each test. The accuracy of the acceleration data reported by the DriveCam systems was assessed by comparing it to the data from the VBOX system. For each set of tests, speeds were also calculated from the DriveCam data and compared to the speeds recorded by the VBOX system.

The image below is a sample of the display available in the DriveCam Event Player and was taken directly from one of the tests discussed in this article (Figure 2). The acceleration data is displayed in the graphs at the top of the display and video from the two cameras is displayed at the bottom.

The time and accelerations are also shown on the display.

Time 0 corresponds to the time when the deceleration rate of the vehicle exceeded the preset threshold. Times prior to

Figure 2: Sample of DriveCam Event Player Display 112 Collision Magazine

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this are designated negative and times after this are positive. The DriveCam systems used for this study recorded 15 seconds of data before and after the triggering events.

M

ethodology

The first set of acceleration and hard braking tests run for this study were conducted on a flat, straight asphalt surface at an abandoned airstrip in Aurora, Colorado on November 28, 2011. The tests were run with a 1994 Toyota Land Cruiser, which is depicted in the first photograph of Figure 3 below. In addition to being equipped with a DriveCam system, this vehicle was instrumented with a VBOX IISX + Slip, Pitch and Roll Angle data acquisition system from Racelogic. The VBOX system utilized three GPS antenna to measure the test vehicle’s translational and angular positions and its speed throughout the tests with a frequency of 20 Hz. These sensors were attached magnetically to racks attached to the roof of the Toyota, as depicted in the second photograph of Figure 3 below. In addition to capturing these tests with the DriveCam system and the VBOX data acquisition system, each test was also captured with two video cameras recording at a rate of 30 frames per second. These cameras were setup at the end of the test area looking back at the approaching vehicle.

The third and fourth photographs of Figure 3 depict the installation of the DriveCam system on the test vehicle.

The particular DriveCam unit that we used in this testing was a model DC3 Cellular Event Recorder. The standard lateral and longitudinal acceleration thresholds on DriveCam systems are 0.5g. For the tests discussed in this article, we kept this standard setting in place for the longitudinal accelerations.

The photograph of Figure 4 depicts the test vehicle situated at its starting position for one of the four tests that we ran with the Toyota Land Cruiser. For all four tests, the Toyota began from a complete stop in a position similar to the one depicted in this photograph. Each test began when the driver of the test vehicle depressed the accelerator pedal and began accelerating the vehicle at a relatively aggressive rate. After accelerating for around 10 seconds, the driver initiated hard braking and brought the vehicle back to a complete stop.

Each of the four tests run with the Toyota resulted in the creation of events for the DriveCam system. The video record and acceleration signals from these events were played back using the DriveCam Event Player. The video record plays back at time intervals of approximately ¼ of a second

Figure 3: Photographs of Toyota Land Cruiser and Instrumentation www.collisionpublishing.com

Collision Magazine 113

erations from the DriveCam system were then integrated to obtain the vehicle speed and this speed was compared to speeds also obtained with the VBOX system.

The second set of acceleration and hard braking tests run for this study were conducted at the same location and in a nearly identical fashion to the first set. These tests were run on March 22, 2012 with a 2002 Chevrolet Cavalier, which is depicted in the first photograph of Figure 5 below.

The additional photographs of Figure 5 depict the setup of the VBOX GPS sensors and of the DriveCam unit on the Chevrolet. The DriveCam unit that we used in this set of tests was a model DC-3000-256-C Cellular Event Recorder. Again, for this set of tests, the triggering accelerations for the DriveCam unit were set at 0.5g for longitudinal acceleration and 0.2g for lateral acceleration.

Figure 4: Test Vehicle Positioned at Start on the Airstrip

and the screen display shows the vehicle accelerations associated with each frame of video. The longitudinal and lateral acceleration signals can also be accessed as graphs within the viewer. For the purposes of this study, the accelerations displayed on these graphs in the DriveCam Event Player were digitized and then used for analysis. These digitized longitudinal accelerations from the DriveCam system were then compared to accelerations for each test obtained with the VBOX system. The longitudinal accel-

A

nalysis of Results

Toyota Land Cruiser Tests The series of graphs in Figure 6 depict the longitudinal accelerations of the Toyota for each of the four runs in the first test series. Each graph contains a curve depicting the accelerations as they were measured by the DriveCam unit (in red) and as they were reported by the VBOX data acquisition system (in blue). In each graph, the horizontal

Figure 5: Photographs of Chevrolet Cavalier and Instrumentation 114 Collision Magazine

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axis gives the time in seconds and the vertical axis gives the acceleration level in gravitational units. For this study, we utilized the times assigned by the DriveCam system and synced the VBOX data to this timing.

As these graphs show, the magnitude of the accelerations reported by the DriveCam unit tended to be slightly lower than the VBOX accelerations while the vehicle accelerated from a stop up to the peak speed and slightly higher than the VBOX during the hard braking. These differences reveal themselves more clearly when the DriveCam accelerations are integrated to obtain the vehicle speed through each test and then again to obtain the distance traversed by the vehicle.

In each of the tests with the Toyota, the vehicle returned to a complete stop before the recording window of the DriveCam system had ended. We were able to determine when the vehicle came to rest based both on the video record and on the acceleration signals. Thus, the acceleration data was integrated back from these known times of rest to ob-

tain an estimate of the vehicle speeds throughout the tests.

The graphs of Figure 7 show the results of performing this integration for each run and compare those results to the speeds of the vehicle as measured by the VBOX. Again, the VBOX results are shown with a blue line and the DriveCam test results are shown with a red line. DriveCam time is plotted on the horizontal axis and speed on the vertical axis.

As these graphs show, there was reasonable agreement between the peak speeds measured by the VBOX and those calculated through integration of the DriveCam accelerations. Table 1 below reports these peak speed values. The graphs in Figure 7 also show, though, that as the integration of the DriveCam data continued back from the peak speed, the divergence between the speeds measured by the VBOX and those obtained from the DriveCam accelerations increased and none of the speed curves obtained from integration of the DriveCam data returned to zero as they should have.

Figure 6: Graphs Comparing DriveCam Acceleration Data to Accelerations from VBOX Data www.collisionpublishing.com

Collision Magazine 115

Table 1: VBOX Peak Speeds Compared to Peak Speeds Calculated from DriveCam Data

the DriveCam video record can be used to supplement the acceleration data by providing physical constraints that analysis of the acceleration data must meet. In other words, integration of the acceleration signals should result in a speed curve that shows the vehicle being stopped at times when the video shows it to be stopped. If the speed curve is inconsistent with what the video shows, then it is possible that a systematic inaccuracy in the DriveCam accelerations could be identified and corrected such that the accelerations yield a velocity curve that meets the known speed constraints.

So far, our analysis of the DriveCam data has treated the accelerometer data in isolation from the video record.

Significant improvements can be made to this analysis by bringing in consideration of what is shown in the video.

In each of the four tests, it was clear from the video record when in time the vehicle began to move away from its initially stopped position and when the vehicle returned to a stop after the hard braking phase (±¼ second). Thus,

To explore this possibility, we experimented with slight upward shifts of the DriveCam acceleration curves prior to integration. This type of correction to the DriveCam acceleration data was suggested by examination of the graphs of Figure 6. It is clear from these graphs that a slight upward shift of the DriveCam accelerations would improve the agreement between the DriveCam and VBOX accelerations. That such a correction will improve the match with the VBOX data may indicate that a DC offset is present

DriveCam Event # 85813 85814 85815 85816

VBOX Peak Speed (mph) 31.4 35.4 40.8 41.3

DriveCam Peak Speed (mph) 32.3 36.7 41.4 42.2

Difference (mph) 0.9 1.3 0.6 0.9

Figure 7: Graphs Comparing Speed from DriveCam Data to Speed Measured by VBOX 116 Collision Magazine

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in the DriveCam acceleration data. In each case, we varied the magnitude of the shift by trial and error and were able to identify a shift that resulted in speed curves consistent with the constraints identified from the video record. The graphs of Figure 8 show the results of integrating these shifted acceleration curves. Table 2 shows the peak speeds calculated from the shifted DriveCam acceleration data, compared to the VBOX peak speed data. By considering the DriveCam video record, we were able to achieve excellent agreement between the speeds obtained from the DriveCam data and the speeds obtained from the VBOX. Figure 9 contains graphs showing the shifted acceleration curves. The shifts in the acceleration curves that were necessary to achieve these improved results for these four tests were as follows: 0.0195g, 0.0195g, 0.011g, and 0.014g.

After adjusting the DriveCam accelerations and integrating them to obtain velocity, the DriveCam and VBOX velocities were integrated once more to determine the distance traveled by the vehicle in each test. Table 3 compares

the total travel distance as calculated from the VBOX and the adjusted DriveCam data.

Chevrolet Cavalier Tests We ran and obtained data for four acceleration and braking tests with the Chevrolet Cavalier. In analyzing these four tests, we followed a procedure identical to that we used for the Toyota tests. The graphs in Figure 10 depict the longitudinal accelerations as reported by the VBOX and DriveCam for the first of the Chevrolet test runs. The graph on the left of Figure 10 contains the unadjusted DriveCam data, whereas the graph on the right contains the adjusted DriveCam data for this test run. As with the previous test series, slight upward shifts of the DriveCam acceleration curves resulted in an improved match with the video record and generally the VBOX speed data. The magnitudes of the acceleration shifts for the four Chevrolet tests were as follows: 0.011g, 0.032g, 0.008g, and 0.021. That said, in the case of the Cavalier tests, the magnitude of the ac-

Figure 8: Graphs Comparing Speeds Obtained from DriveCam Data to Speeds from VBOX (DriveCam Accelerations Adjusted to Impose Known Constraints from Video) www.collisionpublishing.com

Collision Magazine 117

DriveCam Difference DriveCam VBOX Event # Peak Speed Peak Speed (mph) (mph) (mph) 85813 31.4 31.2 0.2 85814 35.4 35.4 0.0 85815 40.8 40.6 0.2 85816 41.3 41.2 0.1

DriveCam VBOX Event # Distance (ft) 85813 321 85814 363 85815 421 85816 404

DriveCam Difference (ft) Distance (ft) 325 4 367 4 424 3 411 7

Table 2: VBOX Peak Speeds Compared to Peak Speeds Calculated from Adjusted DriveCam Data

Table 3: Total Travel Distance as Calculated from VBOX Data and from Adjusted DriveCam Data

Figure 9: Graphs Comparing Adjusted DriveCam Acceleration Data to Accelerations from VBOX Data

118 Collision Magazine

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celerations measured by the DriveCam unit were slightly low during both the acceleration and the deceleration, so the shifting of the curve slightly degraded the match with the peak deceleration, and thus the peak speed, from the VBOX. Figure 11 compares the unadjusted and adjusted velocity curves for the same Chevrolet test run. In this case, adjustment of the DriveCam accelerations improved the match of the analysis with the known stop points of the vehicle, but did not improve the match with the peak velocity reported by the VBOX. This was true for all four of the Chevrolet test runs. Table 4 lists the peak speeds obtained from the VBOX data and those calculated from both the unadjusted and adjusted DriveCam acceleration

data for the Chevrolet tests. With these tests, the differences between the VBOX peak speeds and the calculated DriveCam peak speeds increased after adjustment of the accelerations.

Table 5 compares the total travel distance calculated using the VBOX data to the travel distance calculated using the adjusted DriveCam data.

D

iscussion

The implication of our analysis for the use of DriveCam data within accident reconstruction is that analysis of DriveCam acceleration signals for accident reconstruction purposes should go hand-in-hand with analysis of the video recording. The use of constraints ob-

Figure 10: Graphs Comparing Unadjusted DriveCam and VBOX Acceleration Data to Adjusted DriveCam Acceleration Data

Figure 11: Graphs Comparing Unadjusted DriveCam and VBOX Velocity Data to Adjusted DriveCam Velocity Data www.collisionpublishing.com

Collision Magazine 119

tained from the video recording of each event informed our analysis of the sensor signals and enabled us to obtain reasonably accurate speed curves from the acceleration signals. Thus, the video record should be analyzed to obtain such physical constraints that can be imposed on the speed curves and used to adjust the acceleration data.

In analyzing our tests, we knew that the test vehicles began and ended the tests at rest. In practice, DriveCam events will not always begin or end with a vehicle speed of zero. Thus, some form of video analysis, such as cameramatching photogrammetry, may be necessary to determine the speeds or positions of the vehicle at key times during the event. Once known, these speeds or positions will act to constrain the analysis of the acceleration data.

Shifting the DriveCam acceleration data in the Chevrolet testing did not result in as significant an improvement to the analysis as was the case in the Toyota testing. It is unknown to the authors at this time why the method was more effective for the Toyota tests than for the Chevrolet tests. It is conceivable that additional constraints could be obtained from video analysis, and that the acceleration could be further adjusted to meet these constraints.

Future testing by the authors will explore this issue and will include the testing of multiple DriveCam units in the same vehicle as well as testing one DriveCam unit in multiple vehicles. We also intend to run and analyze tests with swerving vehicle and low-speed impacts. This should allow the authors to further analyze and develop methods that will improve the use of DriveCam data for accident reconstruction.

C

onclusions

• The accuracy of our analysis of the DriveCam data for each of the eight acceleration and hard braking tests was maximized by analyzing the accelerometer signals in conjunction with the video record.

• The use of constraints obtained from the video records enabled us to make necessary adjustments to the accelerations reported by the DriveCam unit. This, in turn, enabled us to obtain reasonably accurate speed curves from the DriveCam acceleration signals.

• Thus, analysis of DriveCam acceleration signals for accident reconstruction purposes should go hand-inhand with analysis of the video record.

A

DriveCam Difference DriveCam VBOX Event # Peak Speed Peak Speed (mph) (mph) (mph) 88670 32.2 30.2 -2.0 88672 32.2 32.7 0.5 88673 32.0 30.6 -1.4 88678 30.8 29.9 -0.9 Table 4: VBOX Peak Speeds Compared to Peak Speeds Calculated from DriveCam Data

DriveCam Difference DriveCam VBOX Event # Peak Speed Peak Speed (mph) (mph) (mph) 88670 32.2 29.5 -2.7 88672 32.2 30.6 -1.6 88673 32.0 30.0 -2.0 88678 30.8 28.4 -2.4 Table 5: VBOX Peak Speeds Compared to Peak Speeds Calculated from Adjusted DriveCam Data

DriveCam VBOX Event # Distance (ft) 88670 346 88672 292 88673 301 88678 355

Table 6: Total Travel Distance as Calculated from VBOX Data and from Adjusted DriveCam Data

tests and in preparation of the data: David Hessel, Tilo Voitel, Daniel Koch and David Pentecost. We would also like to thank the following individuals at DriveCam for providing the DriveCam units utilized in this testing and for taking the time to help us with technical questions related to these units and the interpretation of their data: Del Lisk, Syrus Nemat-Nasser, Phil Schweizer, Monica Mendez and Shelley Bennett.

cknowledgements

The authors would like to acknowledge and thank the following individuals who contributed to this article through their efforts in setting up the hard braking 120 Collision Magazine

DriveCam Difference (ft) Distance (ft) 325 -21 288 -4 288 -13 330 -25

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Peer Review

Raymond M. Brach, R. Matthew Brach, Jarrod Carter, Joseph Marsh, Donald Parker, Chris Van Ee, Michael Varat An Opinion published in Volume 6, Issue 2, of Collision Magazine states that the technical publication process of SAE International (Society of Automotive Engineers) and the organizers of the Accident Reconstruction sessions are controlled by a group of elite individuals that run a peerreview process for technical papers that results in overrated quality, that the review process is meaningless and is in name only. The opinion piece extols a free, open form of publication with no quality control — then proposes a means of establishing a peer-review process for Collision Magazine. It is an opinion of one person and contains no independent supporting information.

A statement attributed to Sir Winston Churchill is as follows: “It has been said that democracy is the worst form of government except for all the others that have been tried.”

The peer-review system used in the 21st century for professional publications can be viewed in an analogous way.

The utility and effectiveness of the peer review system is by no means unanimously respected; the process has proponents and opponents. Recent disputes over climatological research and publications has raised various opinions. For example, Berkowitz [1] says: “What does this scandal [in climatological research] say generally about the intellectual habits and norms at our universities? This is a legitimate question, because our universities, which above all should be cultivating intellectual virtue, are in their day-to-day operations fostering the opposite. Fashionable ideas, the convenience of professors, and the bureaucratic structures of academic life combine to encourage students and faculty alike to defend arguments for which they lack vital information. They pretend to knowledge they don't possess and invoke the authority of rank and status instead of reasoned debate.”

The peer review process is undergoing changes especially in the current age of electronic publication. In a paper on the topic of peer review, J. Scott Armstrong [2] says: “The author’s impression is that the most successful researchers find the current system of journal peer review to be effective. Journal peer review is commonly believed to reduce the number of errors in published work, to serve readers as www.collisionpublishing.com

Op

in

io

a signal of quality and to provide a fair way to allocate journal space. As an editor, referee and author, I cannot recall a single instance in which a journal accepted a paper upon its first submission, without making suggestions for change.

The changes almost always have led to improvements, although sometimes the level of effort and delays seemed excessive when judged against the benefits.”

Stevan Harnard [3] has additional comments on the system of peer review: “Human nature alas cannot be relied upon to police itself. Standards of human performance need to be actively maintained lest they regress on the mean, or even meaner. In learned inquiry, the traditional mechanism of quality control has been "peer review" [4], in which specialists submit their work to qualified adjudicators ('editors'), who in turn select specialists ('referees') to advise them as to whether the material has potential and if so, what further work is needed to make it publishable. A paper is only accepted when the requisite revisions are made to the satisfaction of both editor and referees. This quality control system is far from infallible. Editors can botch or bias the referee selection (most editors would admit that a deliberate bad choice of referees can ensure the acceptance or rejection of any paper) or they can misinterpret or misapply referees' advice. Referees can fail to be sufficiently expert, informed, conscientious or fair. Nor are authors always conscientious in applying the advice of peer review. Indeed, virtually every paper eventually manages to get published, somewhere [5,6]. This produces a hierarchy among journals, based on the rigour of their peer review, with an unrefereed vanity press at the bottom.”

If the policy of a publication’s editors is to allow anything submitted to be published with little or no control, at least a formal system of feedback from readers should exist. If papers are really poor, the feedback system may be embarrassing to the author(s) but in the long run that would be beneficial — the authors would learn to be more careful in their submissions. A claim that “almost” everything that is submitted is published as stated in the opinion article Collision Magazine 121

n

• implies that some reviewing takes place and • is a disservice to those authors whose work is published and certainly is a disservice to the readers.

However, such a policy displays a lack of responsibility of the editorial staff. Bad articles from incompetent authors receive undue recognition and the good often is associated with the bad. What is more tragic is that with a relatively simple valid review, a poor paper can often be significantly improved in many ways, before publication. Reviewers typically point out: • typographical and observational errors, • missing or overlooked acknowledgments and citations to previous work, • use of improper equations or errors in equations, • errors or omissions in diagrams and figures, • missing information and/or data.

Such corrections can make a significant difference to the quality of a paper and benefit the authors, the publication and the readers. If a review is overbearing or incorrect, a qualified editor can always get an additional review or simply overrule the bad review.

The following personal opinions are based on professional careers of writing and reviewing technical papers for domestic and international journals and conferences over a wide variety of technical areas. A good deal of this collective experience has been with SAE. All papers presented and published on accident reconstruction topics at the annual SAE World Congress are organized with the guidance of the Occupant Protection Committee, under the SAE International Land and Sea Group. The quality of the SAE review process has been under continual improvement for the last 20 or more years. Currently, all manuscripts submitted for presentation and publication are: • first submitted by the author(s) in abstract form for preliminary approval, • submitted as a full manuscript for review, • reviewed by three or more reasonably qualified reviewers, • revised by the authors based on the reviewers’ comments; with final approval are accepted for oral presentation at an SAE meeting and/or published as an SAE paper, • a paper rated by reviewers as archival, is considered for publication in SAE International's Scholarly Journals.

Final decisions with respect to publication are made by the session organizers (editors) based on the reviewers’ rankings and comments. This process is followed by all technical divisions of SAE (Propulsion/Powertrain, Safety/

122 Collision Magazine

Testing, Electronics, etc.) including accident reconstruction papers. The criteria used in the SAE review system are substantially the same as other professional journals and publications. The review system represents hard work by many individuals, reviewers and editors, and over the years has been successful in raising the technical and professional level of the publications of SAE. No “elitism” exists in SAE’s review and publication procedure, if only because it would be impossible to build in a bias among thousands of diverse reviewers, who, at times, are not SAE members. Papers published by SAE display high technical and scientific standards and represent the educational and experiential level of authors and reviewers. This high quality is the effect of a reasonably rigorous review process and is causal and real. Reviewing is not passed off to another group or organization.

On a parallel issue, if the (SAE) process of peer review is meaningless and in name only, what about the existing process of Accident Reconstruction certification? Should that process also be questioned? Is it administered by a selfinterested group? Would licensing of Accident Reconstruction be administered more effectively by the States as are the practice of medicine, law, accountancy, engineering, land surveying, etc.?

Our experience with the scientific and engineering review process is almost identical to that of J. Scott Armstrong (above). Well over 90% of the reviews we have received, monitored or observed lead to quality improvements of the papers. Such improvements are beneficial to the authors, the publisher and to the pertinent technical field.

Finally, if a division exists between one or more categories of accident reconstructionist practitioners, the opinion stated in Collision Magazine only aggravates the problem.

References:

1. Peter Berkowitz, Wall Street Journal, Dow Jones, 2010.

2. J. Scott Armstrong, “Peer Review for Journals: Evidence on Quality Control, Fairness and Innovation”, Science and Engineering Ethics, (1997) 3, 63-84.

3. Stevan Harnad, “The Invisible Hand of Peer Review”, http://cogsci.soton.ac.uk/~harnad/intpub.html, December, 2011, Nature © Macmillan Publishers Ltd 1998, Registered No. 785998 England.

4. Harnad, S., “Rational disagreement in peer review”, Sci. Tech. Human Val, 10, 55-62 (1985).

5. Lock, S. “A difficult balance: editorial peer review in medicine”, Nuffield Provincial Hospitals Trust. London, 1985.

6. Harnad, S., “Policing the paper chase”. Nature 322, 24-25 (1986).

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Becoming an Effective Expert Witness Mock Admissibility Hearing Using CDR Accuracy of Toyota EDRs Information Analysis for the Collision Analyst

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