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This paper examines the use of camera-matching video analysis techniques to quantify the vehicle dynamics and deformation for a dolly rollover test run in accordance with the SAE Recommended Practice J2114. The method presented enables vehicle motion data and deformation measurements to be obtained without the use of the automated target tracking employed by existing motion tracking systems. Since it does not rely on this automated target tracking, the method can be used to analyze video from rollover tests which were not setup in accordance with the requirements of these automated motion tracking systems. The method also provides a straightforward technique for relating the motion of points on the test vehicle to the motion of the vehicle's center-of-mass. This paper, first, describes the specific rollover test that was utilized. Then, the camera-matching method that was used to obtain the vehicle motion data and deformation measurements is described.

This paper evaluates the use of PC-Crash simulation software for modeling the dynamics of a dolly rollover crash test. The specific test used for this research utilized a Ford sport utility vehicle and was run in accordance with SAE J2114. Scratches, gouges, tire marks and paint deposited on the test surface by the test vehicle were documented photographically and by digital survey and a diagram containing the layout of these items was created. The authors reviewed the test video to determine which part of the vehicle deposited each of these pieces of evidence. Position and orientation data for the vehicle in the test were then obtained using video analysis techniques. This data was then analyzed to determine the vehicle’s translational and rotational velocities throughout the test. Next, the test was modeled using PC-Crash.

The goal of this paper is to advance rollover crash reconstruction techniques beyond the assumption typically made that a rolling vehicle decelerates at a constant rate. The paper presents and applies a planar vehicle-to-ground impact model to explore the manner in which a vehicle’s deceleration rate would be expected to vary over the course of a rollover. Based on this analysis, several possible variable deceleration rate profile shapes are then suggested for rollover crash reconstruction. Then, two rollover crash tests are analyzed to determine the extent to which these suggested variable deceleration rate profiles can be expected to yield accurate reconstructions of the translational and angular velocity histories for actual rollovers. Overall, each of the suggested variable deceleration rate profiles represented a significant improvement over using a constant deceleration rate.

In a 2012 paper, Brach, Brach, and Louderback (BBL) investigated the uncertainty that arises in calculating the change in velocity and crush energy with the use of the CRASH3 equations (2012-01-0608). They concluded that the uncertainty in these values caused by variations in the stiffness coefficients significantly outweighed the uncertainty caused by variations in the crush measurements. This paper presents a revised analysis of the data that BBL analyzed and further assesses the level of uncertainty that arises in CRASH3 calculations. While the findings of this study do not invalidate BBL's ultimate conclusion, the methodology utilized in this paper incorporated two changes to BBL's methodology. First, in analyzing the crash test data for several vehicles, a systematic error that is sometimes present in the reported crush measurements was accounted for and corrected.

A simple two-dimensional particle model was previously developed to calculate occupant ejection trajectories in rollover crashes. Model parameters were optimized using data from a dolly rollover test of a 1998 Ford Expedition in which five unbelted anthropomorphic test devices (ATDs) were completely ejected. In the present study, the model was further validated against a dolly rollover test of a 2004 Volvo XC90 in which three unbelted ATDs were completely ejected. The findings from the experimental testing were then compared to two real world rollover crashes with occupant ejections that were captured on video. The crashes were reconstructed by analyzing the video footage and aerial images of the crash sites. In both cases, the model was able to accurately match the observed trajectories of the ejected occupants, and the optimized model parameters were similar to the values obtained from the dolly rollover testing.

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.

This paper describes, demonstrates and validates a method for incorporating the effects of restitution into crush analysis. The paper first defines the impact coefficient of restitution in a manner consistent with the assumptions of crush analysis. Second, modified equations of crush analysis are presented that incorporate this coefficient of restitution. Next, the paper develops equations that model restitution response on a vehicle-specific basis. These equations utilize physically meaningful empirical constants and thus improve on restitution modeling equations already in the literature of accident reconstruction. Finally, the paper presents analysis of four vehicle-to-vehicle crash tests, demonstrating that the application of the restitution model derived in this paper results in crush analysis yielding more accurate ΔV calculations.

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.