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This paper presents an in-depth analysis of the performance of Event Data Recorders (EDRs) found in the Airbag Control Modules (ACMs), as tested in support of the National Highway Traffic Safety Administration’s Frontal Oblique Offset Program. Previous studies have examined EDR performance in high severity full-frontal barrier crash tests and moving deformable barrier side impact tests. This paper presents data from a high severity oblique frontal impact test in which the vehicle was struck by a moving deformable barrier. This paper examines the results of EDR data downloaded from two 2015 model year Toyota Highlanders, and the results of EDR reported change in velocity (delta-v), to vehicle mounted accelerometers and reference instrumentation. This paper will analyze EDR performance in reporting: Seatbelt buckle status, Occupant Size Classification, Front Passenger, Airbag and seatbelt pretensioner deployment time(s), Longitudinal delta-v, and Lateral acceleration/crash pulse.

This paper presents event data from the Sensing and Diagnostic Module (SDM) of a 2004 Chevrolet Malibu during high speed yaw testing. Yaw tests were performed using tires that were intact and tires that had the tread removed. The tires that had the tread removed were placed at various wheel positions on the vehicle (e.g. leading side - front, leading side -rear, trailing side - rear). This testing simulates the loss of control phase subsequent to a tread separation. Speeds up to 117 km/h (72.9 mph) were achieved. A simple electro-mechanical device was incorporated to the dynamic testing to simulate a low-severity non-deployment event that triggered the recording of pre-crash data by the SDM. The SDM data from the tests was imaged and compared to reference data from vehicle-mounted instrumentation recording wheel speed, steering angle, measured vehicle sideslip angle and GPS calculated over the ground speed.

This paper presents yaw testing of vehicles with tread removed from tires at various locations. A 2004 Chevrolet Malibu and a 2003 Ford Expedition were included in the test series. The vehicles were accelerated up to speed and a large steering input was made to induce yaw. Speed at the beginning of the tire mark evidence varied between 33 mph and 73 mph. Both vehicles were instrumented to record over the ground speed, steering angle, yaw angle and in some tests, wheel speeds. The tire marks on the roadway were surveyed and photographed. The Critical Speed Formula has long been used by accident reconstructionists for estimating a vehicle’s speed at the beginning of yaw tire marks. The method has been validated by previous researchers to calculate the speed of a vehicle with four intact tires. This research extends the Critical Speed Formula to include yawing vehicles following a tread detachment event.

When a vehicle with protruding wheel studs makes contact with another vehicle or object in a sideswipe configuration, the tire sidewall, rim and wheel studs of that vehicle can deposit distinct geometrical damage patterns onto the surfaces it contacts. Prior research has demonstrated how relative speeds between the two vehicles or surfaces can be calculated through analysis of the distinct contact patterns. This paper presents a methodology for performing this analysis by visually modeling the interaction between wheel studs and various surfaces, and presents a method for automating the calculations of relative speed between vehicles. This methodology also augments prior research by demonstrating how the visual modeling and simulation of the wheel stud contact can extend to almost any surface interaction that may not have any previous prior published tests, or test methods that would be difficult to setup in real life.

The data obtained from event data recorders found in airbag control modules, powertrain control modules and rollover sensors in passenger vehicles has been validated and used to reconstruct crashes for years. Recently, a third-party system has been introduced that allows crash investigators and reconstructionists to access, preserve and analyze data from infotainment and telematics systems found in passenger vehicles. The infotainment and telematics systems in select vehicles retain information and event data from cellular telephones and other devices connected to the vehicle, vehicle events and navigation data in the form of tracklogs. These tracklogs provide a time history of a vehicle’s geolocation that may be useful in investigating an incident involving an automobile or reconstructing a crash. This paper presents an introduction to the type of data that may be retained and the methods for performing data acquisitions.

This paper presents a comprehensive literature review of original equipment event data recorders (EDR) installed in passenger vehicles, as well as a summary of results from the instrumented validation studies. The authors compiled 187 peer-reviewed studies, textbooks, legal opinions, governmental rulemaking policies, industry publications and presentations pertaining to event data recorders. Of the 187 total references, there were 64 that contained testing data. The authors conducted a validation analysis using data from 27 papers that presented both the EDR and corresponding independent instrumentation values for: Vehicle velocity change (ΔV) Pre-Crash vehicle speed The combined results from these studies highlight unique observations of EDR system testing and demonstrate the observed performance of original equipment event data recorders in passenger vehicles.

Previous work demonstrated that the orientation of tire mark striations can be used to infer the braking actions of the driver [1]. An equation that related tire mark striation angle to longitudinal tire slip, the mathematical definition of braking, was presented. This equation can be used to quantify the driver’s braking input based on the physical evidence. Braking input levels will affect the speed of a yawing vehicle and quantifying the amount of braking can increase the accuracy of a speed analysis. When using this technique in practice, it is helpful to understand the sensitivity and uncertainties of the equation. The sensitivity and uncertainty of the equation are explored and presented in this study. The results help to formulate guidelines for the practical application of the method and expected accuracy under specified conditions. A case study is included that demonstrates the analysis of tire mark striations deposited during a real-world accident.

This paper reports a method for analyzing data from a DriveCam unit to determine impact speeds and velocity changes in vehicle-to-vehicle impacts. A DriveCam unit is an aftermarket, in-vehicle, event-triggered video and data recorder. When the unit senses accelerations over a preset threshold, an event is triggered and the unit records video from two camera views, accelerations along three directions, and the vehicle speed with a GPS sensor. In conducting the research reported in this paper, the authors ran four front-to-rear crash tests with two DriveCam equipped vehicles. For each test, the front of the bullet vehicle impacted the rear of the stationary target vehicle. Each of the test vehicles was impacted in the rear twice - once at a speed of around 10 mph and again at a speed around 25 mph. The accuracy of the DriveCam acceleration data was assessed by comparing it to the data from other in-vehicle instrumentation.

This paper details a method for obtaining the coefficient of restitution from a vehicle-to-vehicle crash test and for quantifying the uncertainty in the resulting value. The coefficient of restitution is determined by analyzing accelerometer data to obtain the post-impact velocity conditions for the test and, by then, using the method of least squares to fit an impulse-momentum solution to the results of the accelerometer data analysis. Uncertainties that affect the accelerometer data analysis include uncertainties associated with the acceleration readings and the accelerometer locations within the vehicle-fixed coordinate system. Uncertainties that affect the fit between the impulse-momentum solution and the post-impact velocity conditions include uncertainty associated with the vehicle weights and moments of inertia and uncertainty associated with the placement of the impact center.