Issuu

2014 ARC--CSI DATA DVD

2014 Vol.9 Iss.2

ollision C The International Compendium for Crash Research

Volume 9 Issue 2

FALL 2014

Collision: The International Compendium for Crash Research

Using Inequality Contraints in the Probability Analysis of

Motor Vehicle Accidents

Volume 9, Issue 2 - FALL 2014

Ignition Switch Data in CDR Reports

“Low Velocity” Testing collisionmagazine.com

Contents

Fall 2014

Volume 9 Issue 2 8

inside 4

Letter From the Editor

NAPARS: Letter From the President

features 8

Using Inequality Constraints in the Probability Analysis of Individual Motor Vehicle Accidents by: Scott Kimbrough

Wheel Slip and Its Effect on Reported Vehicle Speed by: David M. Hallman

How Long Can You Stay Awake? by: Clinton Marquardt

Low Velocity Piston Isolator Testing of a Ford Crown Victoria by: Lissette Ruberte, Susan Lantz, Robert Thompson, Michael Chan, Bobby Clemence, Greg Dycus, Mitchell Burton, Brian Chan, Terry Wong, and Billy Cox

Vehicle Dynamics and Resultant Occupant Accelerations Caused by Vehicle Wheel Separation by: C. Brian Moody, Orion P. Keifer, Bradley C. Reckamp, and Wes Richardson

CDR Report Data from Vehicles Subject to the GM Ignition Switch Recall with the “Epsilon” ACM by: W. R. Rusty Haight

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Determination of Motorcycle PreCollision Speed - Part 1 & 2 by: Oren Masory, Wade Bartlett, and Bill Wright

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Using Inequality Constraints in the Probability Analysis of Individual Motor Vehicle Accidents Scott Kimbrough MRA Forensic Sciences

bstract Accident analysis often involves using derivations to convert raw input information into more useful and comprehensible information. For example, analyzing a motor vehicle accident often involves converting raw inputs such as the lengths and directions of tire marks into more immediately meaningful outputs such as the speeds of vehicles at impact. Newton’s laws are often used in such derivations. Abstractly, this process can be thought of as involving three entities, namely: 1) an input space containing information about an accident; 2) a set of derivation procedures; and 3) an output space containing the results of the derivations. Each derivation procedure “maps” points in its input space to corresponding points in its output space; thusly, the terms “derivation procedure” and “mapping” will be used interchangeably. 8 Collision Magazine - Volume 9 Issue 2

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Often some of the most important information about an accident can take the form of inequality constraints that place boundaries on things like magnitudes and when and where events happened. Such inequality constraints can apply to input variables, output variables, or both. When the constraints apply to the input variables, they can be applied directly to limit the inputs. However, when the constraints apply to output variables, the derivation must usually be carried out before the constraints can be applied. When inequality constraints are applied to the outputs, they separate the output space into feasible regions and infeasible regions, where the feasible regions of the output space contain those outputs that satisfy the constraints. At the same time, separating the output space into feasible and infeasible regions also implicitly separates the input space into corresponding feasible and infeasible regions, where the feasible regions of the input space contain those inputs that map to feasible outputs. www.collisionpublishing.com

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heel Slip David M. Hallman, M.S., P.E.

bstract On ice, on wet roads, in the air ... in the right conditions, wheels can slip and when they do there is the potential for a reported speed (whether on the speedometer or in EDR related data) to be different than the actual vehicle speed at the time the information was recorded. This article will look at the sources of reported vehicle speed such as vehicle speed sensors and wheel speed sensors and some of the conditions which might affect the reported speed. Based on prior testing performed coupled with testing done at the 2014 ARC-CSI Crash Conference, this article will explore the causes and implications of wheel slip and some of the steps a reconstructionist might take to account for the phenomenon where appropriate. Also discussed here are recently added (by the manufacturer) data recording and storage features specific to some Toyota vehicles which were discovered during vehicle testing.

ntroduction Crash investigators have many different sources of information regarding what occurred in the moments leading up to and immediately following a crash. One of those sources is the retrievable data stored in the airbag control module (ACM) in a steadily increasing number of vehicles on the road. Crash data has gained wide acceptance among experts, attorneys and the court system 9 as being an accurate representation of certain aspects of vehicle performance and operator behavior in the moments leading up to a crash as well as containing crash and postcrash information. It is important to understand that the stored ACM data reflects a particular sensor output and interpretation of that output at the time it was recorded, but may not accurately represent the behavior of the physical vehicle in all cases.

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As defined by the Code of Federal Regulations (CFR) Title 49, Part 563, vehicle speed in the stored crash data is required to be the indicated vehicle speed. Depending on the year, make and model of the vehicle, indicated vehicle speed could be supplied by a speed sensor in the transmission or wheel speed sensor(s). It could be reported from those sensors to the ACM via the engine controller, instrument panel controller, body controller, or a variety of other modules on the vehicle. If a wheel or wheels are spinning, slipping or locked prior to, during, or immediately after a crash the speed information may not be directly indicative of actual vehicle speed. The Data Limitations text from most Bosch CDR reports states “SDM (ACM) Recorded Vehicle Speed accuracy can be affected by various factors, including but not limited to the following…wheel lockup and wheel slip" (or words to that effect). An example of crash data collected where ACM reported speed was suspect is shown in Figure 1. In the case of this GM non-deployment data, vehicle indicated speed is shown between 103 miles per hour and 106 miles per hour in the four seconds leading up to algorithm enable (AE) and 20 miles per hour in the final second with an increase in throttle percentage from 25% to 49%. How is this possible? More importantly, how could this be a nondeployment event with an indicated speed decrease of 83 miles per hour reported from 2 seconds to 1 second? This data was collected from a run-off-road crash which occurred on a two lane road in rural North Dakota. The vehicle operator first crossed over the oncoming travel lane (which was empty at the time) and drove off of the pavement. After leaving the roadway and driving down into the far side ditch it struck a dirt “ramp” which was put in place to allow a farmer to drive equipment into his field without having to drive down through the steep ditch

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along the roadway. This ramp was oriented perpendicular to the paved roadway and the parallel ditch. When the vehicle struck the ramp, the unbelted driver slid forward in the seat resulting in the increased throttle percentage. At the same time, the front wheel rotation slowed dramatically but the vehicle did not, resulting in a very low reported speed with an actual vehicle speed that was significantly higher. After striking the dirt ramp, the vehicle became airborne for approximately 250 feet and then impacted the ground and rolled multiple times end over end, ejecting and killing the unbelted driver. This article will begin by discussing some reasons why wheels can slip and how wheel slip is controlled by different vehicle systems depending on the type of slip which is occurring. Three different systems are included in modern vehicles for limiting and controlling wheel slip under various conditions. Those systems are antilock braking systems (ABS), traction control systems (TCS) and the recently mandated 4,5 electronic stability control (ESC). These systems, how they operate and federal regulations governing them will also be discussed. Data which was collected from several different vehicles to examine the effects of wheel slip on reported vehicle speed will be examined. During testing on a 2013 Toyota Rav4, an interesting new feature specific to some Toyota vehicles was discovered. The 2013 - 2015 Toyota Rav4, the 2014 - 2015 Toyota Highlander and 2014 - 2015 Highlander Hybrid all record data in various modules when

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How long can you stay awake?

Clinton Marquardt Human Fatigue Specialist

he longest well-documented period without sleep is 264 hours and 12 minutes…about 11 days1. Seventeen year old Randy Gardner broke the previous record of 260 hours for his highschool science fair project. This was back in 1965 and there hasn’t been anything like it since.

mental and physical tests remained remarkably intact. In fact, William Dement, the grandfather of sleep medicine and originator of the term “REM sleep”, tells a story of how Randy repeatedly beat him on a baseball game at a penny arcade in the middle of the night.1

What was amazing about Randy’s Guinness Book of What was not so amazing, was that the longer Randy World Record breaking feat, stayed awake, the harder it was to keep him from was that his ability to falling asleep. Randy was fatigued. perform well “Fatigue” is used to describe on many many different human states. Here are six uses of the term:

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At 264 hours and 12 minutes of wakefulness, it would be hard to argue that Randy’s hand-eye coordination 2. Physical Fatigue = a human physical state re- would have been betsulting from body movements like exercise. ter than someone with 3. Lethargic Fatigue = a human psychological a BAC of 0.05%. If Randy’s performance was worse and physiological condition of lethargy that than this, should he be trusted to drive a car? If not, can result from a number of illnesses such as when would you tell Randy to stop driving and get some sleep? depression or the common cold. 4. Receptor Fatigue = a biological response to The threshold where almost all human performance over stimulation of one of the senses; for ex- measures decline rapidly is 22 hours. This is because ample, the olfactory receptors will fatigue af- it becomes impossible to maintain your awake state ter prolonged exposure to a smell, this results (a.k.a. state instability) at 22 hours of continuous in a temporary inability to distinguish that wakefulness. Micro-sleeps, short periods of sleep last-4 ing three to four seconds, begin at this threshold specific smell. providing that countermeasures such as strategic use 5. Task Fatigue = a point of diminishing returns, of caffeine and alertness sustaining medications have where the effort required to stay on task is not been ingested or you don’t have Randy’s stimulagreater than the effort required to move for- tion team sitting beside you in the car. ward on the task. For accident reconstructionists, the 17 and 22 hour 6. Sleep related fatigue = a human biological thresholds can serve as indicators for investigating fatigue. At 17 hours of wakefulness, fatigue should be state of sleepiness. considered a potential contributing factor and other Randy was experiencing sleep-related fatigue. There sources of fatigue should be investigated to determine is a range in sleep-related fatigue levels. At the lowest the likelihood that fatigue may have played a role in fatigue level you would feel wide-awake you would the accident. At 22 hours of wakefulness, a full invesfind it hard to fall asleep within ten minutes.2 At the tigation of fatigue should be considered. Fatigue will other end of the range, the extreme fatigue level, like most likely be a contributing factor. Randy, you would find it really difficult to stay awake eferences and, without someone forcing you to stay active and 2 1. Dement, W. (1972). Some must watch aroused, you would fall asleep within five minutes. while some must sleep. San Francisco: W.H. Randy’s team of friends and researchers kept him Freeman and Company. stimulated and awake. Without your own stimulation 2. For more information on measuring sleepiness team, just when should you cut off the wakefulness see: Carskadon, M., Dement, W., Mitler, M., and think about getting some sleep? Roth, T., Westbrook, P., & Keenan, S. (1986). Guidelines for the multiple sleep latency test Back in 1997, a re(MSLT): A standard measure of sleepiness. Sleep, search team from 9(4), 519-524. Australia compared 3. Dawson, D., & Reid, K. (1997). Fatigue, alcohol cognitive psychoand performance impairment. Nature, 388, 235. motor performance 4. Beaumont, M., Batejat, D., Pierard, C., Coste, after long periods O., Doireau, P., Van Beers, P., Chauffard, F., of wakefulness to Chassard, D., Enslen, M., Denis, J., & Lagarde, performance after D. (2001). Slow release caffeine and prolonged drinking alcohol.3 What they found was that if you (64-h) continuous wakefulness: Effects on vigistay awake for 17 hours, your hand-eye coordination lance and cognitive performance. Journal of Sleep would be about the same as if you drank enough alcoResearch. 10(4), 265-276. hol to bring your blood alcohol concentration (BAC) level to 0.05%. 1. Mental Fatigue = a human psychological state resulting from spending extended or intense periods of time on a task like studying for an exam.

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Low Velocity Piston Isolator Testing of a Ford Crown Victoria

Lissette Ruberte Robert Thompson Bobby Clemence Mitchell Burton Terry Wong

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Susan Lantz Michael Chan Greg Dycus Brian Chan Billy Cox

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ntroduction

Accident reconstructionists are often asked to determine the change in velocity (delta-V) sustained by passenger cars with minimal visible damage to the bumper assembly beyond scratches and scuff marks to the plastic bumper cover. In vehicles equipped with energy-absorbing piston isolators, evidence marks on the exposed piston tube in conjunction with isolator compression data can be used to estimate the change in velocity experienced by the vehicle. However, bumper specific data relating isolator compression to changes in velocity of the vehicle are often unavailable. Trends in isolator performance specific to vehicle manufacturers have previously been reported by Siegmund et al. (1994) based on over 600 vehicle-to-vehicle and vehicle-tobarrier collisions. In that study, several of the vehicles tested were within the 1980 to 1982 model year, when US Federal Motor Vehicle Safety Standards required that passenger cars sustain 5-mph longitudinal barrier and 3-mph pendulum impacts without sustaining significant structural damage. In 1982, the standard was lowered to 2.5-mph longitudinal barrier impacts and 1.5-mph corner pendulum impacts (La Heist and Ephraim, 1987). Of interest is whether the dynamic behavior of the piston isolators in the 2007 Ford Crown Victoria compares with the isolator data reported in the earlier work by Siegmund et al. (1994). A series of low-speed, full-width, rear-into-barrier impacts were conducted with a 2007 Ford Crown Victoria. The tests were conducted on a single day as part of the ARC-CSI Boot Camp Experience. Two instrumented females were seated in the driver seat, one for three tests, the other for five tests. Neither occupant reported any pain or symptom of injury at the three-day posttesting follow-up examination. This article focuses on the dynamic behavior of the Ford Crown Victoria piston isolators.

ethods

Eight full-width vehicle-into-barrier tests were conducted with a 2007 Ford Crown Victoria (VIN: 2FAFP72V47X147799, weight: 1853 kg). The vehicle was originally part of a police fleet, then a taxi cab fleet.

The rear bumper assembly of the 2007 Ford Crown Victoria consisted of a pair of energyabsorbing piston isolators with an exposed piston tube length of approximately 40 mm. The bumper assembly was inspected prior to testing and showed no evidence of prior structural damage or repairs. The vehicle was subjected to sequential barrier impacts with increasing closing speed to achieve changes in velocity (delta-V) of 1.0 to 2.6 m/s (1.7 to 5.8 mph). The repeatability of Ford (Mercury) piston isolators in low-speed impacts has previously been questioned (King et al., 1993). In order to verify the repeatability of the tests results, two series of sequential barrier impacts were conducted with the same vehicle. The first sequence consisted of three impacts with a closing speed range of 0.56 to 1.38 m/s (1.3 to 3.1 mph). The second sequence consisted of five impacts with a closing speed range of 0.46 to 1.77 m/s (1.0 to 4.0 mph). These changes in velocity were selected to develop a detailed profile of isolator compression versus velocity change. At this severity level, the vehicle was expected to withstand multiple impacts without sustaining structural damage. The barrier consisted of a stack of ten concrete barrier blocks. The impact surface of the barrier was covered with a 13-mm thick sheet of plywood. Each successive impact occurred within 2 to 5 minutes. The vehicle’s transmission was put in neutral, and the vehicle was accelerated to the desired closing speed by manually pushing the vehicle while monitoring a digital speed readout (VBOX). The vehicle was released less than two feet before impact and struck the stationary rigid barrier with the full bumper width. After each impact, the rear bumper was inspected and photographed. As shown in Figure 1, a plastic strap was added to each piston at the compression point, and post-impact displacement was measured to the nearest 0.8 mm (1/32 inch).

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Vehicle Dynamics and Resultant Occupant Accelerations Caused by Vehicle Wheel Separation

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C. Brian Moody P.E., Bradley C. Reckamp P.E.,

Orion P. Keifer P.E., Wes Richardson

bstract Passenger car wheels are typically attached to the suspension hubs with lug nuts threaded onto wheel studs, or with lug bolts threaded into mating female threads in the hubs. These threaded connections can, for various reasons, fail. Such a failure often causes the wheel and tire to separate from the vehicle while in motion. This separation invariably results in the vehicle falling onto its suspension / brake components, and in many cases, an impact between the ejected tire and the vehicle. Accelerations imposed on the vehicle’s occupants during such incidents have been blamed for injuries and have been the subject of personal injury litigation. In order to better understand the accelerations and forces experienced by vehicle occupants during wheel separation from a moving vehicle, testing was performed involving a 1988 Nissan Maxima and further testing of a 1998 Ford Escort sedan. The series of Nissan tests included a static test set, in which one of the Nissan’s rear wheels was removed and the hub was lifted and dropped from various heights, as well as a dynamic test set, in which one of the rear wheels was caused to eject while in motion. The series of Ford tests included a dynamic test set in which a wheel was caused to eject while the Ford was traveling at various speeds, as well as a second dynamic test set in which the Ford was driven over speed

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bumps and parking lot wheel stops at various speeds. Acceleration data experienced by the vehicle was recorded during all tests. This data was then compared to various activities with vertical acceleration and existing design standards for amusement park rides.

ntroduction A wheel may detach from a passenger vehicle in motion due to a variety reasons. Such accidents occasionally result in loss of control of the vehicle leading to a collision, for which standard collision reconstruction techniques are applicable. Invariably, however, the wheel ejection also results in an impact between the suspension, or brake components, and the pavement and possibly between the ejected wheel and the vehicle as the wheel departs. The forces from these impacts have been the focus of many personal injury claims over the years. In a common analytical approach, a reconstructionist uses the Impact-Momentum equation, that is the change in momentum (mass times change in velocity) is equal to the impulse (force times time over which the force is applied). Generally, a drop in height was assumed in order to calculate the impact velocity/change in momentum and an assumed impact time duration of the impact is used to calculate the force on the vehicle. In practice, the calculated force can

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CDR Report Data from Vehicles Subject to the GM Ignition Switch Recall with the “Epsilon” ACM

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W. R. Rusty Haight Collision Safety Institute

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ntroduction Crash data retrieved from a vehicle which is identified as part of the “GM ignition switch recall” may contain data parameters which may be associated with the type of failure detailed in the recall.1 The focus of this article is on identifying those parameters, understanding their impact on the remaining data elements which may be retrieved and potentially eliminating data which is not associated with the ignition switch failure. This article will explore how the ignition switch function is related to the data stored in a specific airbag control module found in those vehicles associated with the recall and what the “normal” versus “abnormal” data states are for some of the data elements retrieved from the identified vehicles in the recall group. This article is not intended to rehash all the underlying issues with the ignition switch as described in the recall. For the purposes of this narrative, one should assume that the description of the ignition switch “issues” are discussed sufficiently in the recall and elsewhere including the Valukas report 2 and press releases 3 and hearing information from the Energy and Commerce Committee of the US House of Representatives, 113th Congress. The focus here is on the interpretation and use of data from a subset of the vehicles (more specifically, data from the airbag control module type found in those vehicles) associated with the recall in a crash reconstruction context. In the preparation of this article, the author has relied on publically available information such as the “document binder” materials from the Energy and Commerce Committee 4 and resources/references listed in those documents, crash tests conducted at the 2014 ARC-CSI Crash Conference,5 the NHTSA Recall documents, and a review of more than 200 CDR data files from a variety of vehicles/modules.

cope Six vehicle models (some are sisters/ clones) are identified in the NHTSA Recall. They are:

•

2005-10 Chevrolet Cobalt

• 2006-11 Chevrolet HHR • 2007-10 Pontiac G5 • 2006-10 Pontiac Solstice • 2007-10 Saturn Sky • 2003-07 Saturn Ion Of these, the Saturn Ion was equipped with a different Airbag Control Module (ACM) than that found in the remaining vehicle models. The Ion ACM data set does not include all the same data elements found in the ACM installed in the remaining vehicles and an analysis of data from an Ion’s ACM should be handled differently than that recovered from the remaining vehicles/modules identified in the recall. By way of further identification in this respect, we might turn to two sources of information regarding those vehicles and the ACM type(s). First, the Bosch CDR Tool Help file 6 can be used to identify the cable which would be used if one were to image data from an ACM found in an Ion or one of the other listed vehicles when connected “direct-to-module.” The direct-to-module cable for the Ion is identified in the Help File as cable number 02003004. The direct-to-module cable number called for in the Help File for the remaining listed vehicles is the 02003321 cable. The Help File cable number can be thought of as generally identifying a “family” of ACMs which may be found in a variety of vehicles.

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Determination of Motorcycle Pre-Collision Speed Oren Masory

Department of Ocean and Mechanical Engineering, Florida Atlantic University

Wade Bartlett

Mechanical Forensics Engineering Services

Bill Wright

Institute of Police Technology and Management

Part I : Physical Models

bstract The purpose of this paper is to evaluate different formulas, based on principles of mechanics, which are used to estimate motorcycle’s precollision speed in motorcycle-to-car accidents. These formulas use measurable physical parameters such as motorcycle’ wheelbase and car crush depth which were obtained from experiments performed by the authors as well as published experimental data. The results of these evaluations indicate that the motorcycle’s precollision speed cannot be estimated with high accuracy using these formulas. Therefore, during accident investigation, additional information is needed for accurate reconstruction.

ntroduction According to the National Highway Traffic Safety Administration (NHTSA), in 1969, there were 2.3 million motorcycles registered in the United States. This number increased dramatically during the next 40 years and currently there are over seven million motorcycles registered in the United States. Obviously, as the number of motorcycles on the road increased, so did the accident rate. In 1969 69,500 motorcycle

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accidents were reported, in which 1,855 persons were killed. In 2004, 4,008 motorcyclists were killed in the US, a 116% increase from 1969. The death rate per 100 million vehicle miles traveled for a motorcyclist is over 35 times higher than of an automobile occupant 1. One of the main and unequivocal reasons motorcyclists are killed in crashes is because the motorcycle itself provides virtually no protection in a crash. For example, approximately 80 percent of reported motorcycle crashes result in injury or death while a comparable figure for automobiles is about 20 percent. The earliest and the most frequently cited testing was published by Severy in 1970 2, which included one test at 20mph, one test at 40mph, and 5 tests at 30mph. That paper included a chart showing a linear relationship between wheelbase reduction and the motorcycle’s pre-collision speed with high correlation factor of 0.975. The Severy data is not relevant to today's motorcycles: chassis design, wheels, materials, engine mounting, and front end suspension styles have changed enough that his data is not applicable for today motorcycles.

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In 1976, Professor Harry Hurt3 of the University of Southern California conducted a study on 3,600 motorcycle accidents in the Los Angeles area. The results of this study have become the basis for much motorcycle rider training and research. The study found that in 65 percent of the multi-vehicle cases the automobile violated the motorcycle right of way. A similar survey conducted by the Philadelphia Police Department showed

that such a violation is the cause in only 45 percent of all motorcycle accidents in the city of Philadelphia. Hurt’s study also found that in 64 percent of single-vehicle motorcycle accidents, the operator was responsible for his accident. The majority of accidents occurred on a clear, dry day during daylight hours and typically, the motorcycle operator had less than six months riding experience and no formal training.

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