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Journal Article

Nonlinear Optimization in Vehicular Crash Reconstruction

This paper presents a reconstruction technique in which nonlinear optimization is used in combination with an impact model to quickly and efficiently find a solution to a given set of parameters and conditions to reconstruct a collision. These parameters and conditions correspond to known or prescribed collision information (generally from the physical evidence) and can be incorporated into the optimized collision reconstruction technique in a variety of ways including as a prescribed value, through the use of a constraint, as part of a quality function, or possibly as a combination of these means. This reconstruction technique provides a proper, effective, and efficient means to incorporate data collected by Event Data Recorders (EDR) into a crash reconstruction. The technique is presented in this paper using the Planar Impact Mechanics (PIM) collision model in combination with the Solver utility in Microsoft Excel.

Rollover Crash Reconstruction

According to the National Highway Traffic Safety Administration, “of the nearly 9.1 million passenger car, SUV, pickup and van crashes in 2010, only 2.1% involved a rollover. However, rollovers accounted for nearly 35% of all deaths from passenger vehicle crashes. In 2010 alone, more than 7,600 people died in rollover crashes.” Rollover accidents continue to be a leading contributor of vehicle deaths. While this continues to be true, it is pertinent to understand the entire crash process. Each stage of the accident provides valuable insight into the application of reconstruction methodologies. Rollover Accident Reconstruction focuses on tripped, single vehicle rollover crashes that terminate without striking a fixed object.
Technical Paper

Bayesian Uncertainty Quantification for Planar Impact Crashes via Markov Chain Monte Carlo Simulation

A continuing topic of interest is how to best use information from Event Data Recorders (EDR) to reconstruct crashes. If one has a model which can predict EDR data from values of the target variables of interest, such as vehicle speeds at impact, then in principle one can invert this model to estimate the target values from EDR measurements. In practice though this can require solving a system of nonlinear equations and a reasonably flexible method for carrying this out involves replacing the inverse problem with nonlinear least-squares (NLS) minimization. NLS has been successfully applied to two-vehicle planar impact crashes in order to estimate impact speeds from different combinations of EDR, crush, and exit angle measurements, but an open question is how to assess the uncertainty associated with these estimates. This paper describes how Markov Chain Monte Carlo (MCMC) simulation can be used to quantify uncertainty in planar impact crashes.

Motorcycle Accident Reconstruction

In a recent National Highway Traffic Safety Administration (NHTSA) report, about one out of every 7 fatalities on the road involved a motorcycle. Itis clear that motorcyclists are more vulnerable and much more likely to be injured or killed in a crash than are passengers in a car accident. Motorcycle Accident Reconstruction purposefully pulls together as much of the relevant accident reconstruction literature and science as possible to present definitive literature that meets the needs of the crash reconstruction industry. The reader will learn to analyze physical evidence, understand what it means, and how to incorporate math and physics into an investigation. Topics featured in this book include: Case studies utilizing event data recorder data Photogrammetry analysis Determining motorcycle speed at the time of an accident The book provides a unique roadmap for the motorcycle accident reconstructionist user.