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Crash Reconstruction Of Pedestrian Accidents Using Optimization Techniques

C. Untaroiu, M. Meissner, J. Crandall, Y. Takahashi, M. Okamoto, O. Ito
Published 2009 · Engineering

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Numerical simulations of vehicle-to-pedestrian crash (VPC) are frequently used to develop a detailed understanding of how pedestrian injuries relate to documented vehicle damage. Given the complexity of the event, modeling the interactions typically involves subjective evaluations of the pre-impact conditions using a limited number of simulations. The goal of this study is to develop a robust methodology for obtaining the pre-impact pedestrian posture and vehicle speed utilizing multi-body simulations and optimization techniques. First, a continuous sequence of the pedestrian gait based on the literature data and simulations was developed for use as a design parameter during the optimization process. Then, the robustness and efficiency of three optimization algorithms were evaluated in a mock (idealized) crash reconstruction. The pre-impact parameters of the pedestrian and the vehicle models were treated as unknown design variables for the purpose of validating the optimization technique. While all algorithms found solutions in close vicinity of the exact solution, a genetic algorithm exhibited the fastest convergence. The response surfaces of the objective function showed higher sensitivities to the pedestrian posture and its relative position with respect to the vehicle than to the vehicle speed for the chosen design space. After validating the methodology with the mock reconstruction, a real-world vehicle-to-pedestrian accident was reconstructed using the data obtained from the field investigation and the optimization methodology. A set of pedestrian and vehicle initial conditions capable of matching all observed contact points was determined. Based on the mock and real-world reconstructions, this study indicates that numerical simulations coupled with optimization algorithms can be used to predict pedestrian and vehicle pre-impact conditions.
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