Occupant Injury Response Prediction Prior to Crash Based on Pre-Crash Systems
Xiao Mei Luo, Wenjing Du, Hao Li, Peiyu Li, Chunsheng Ma, Shucai Xu, Jinhuan Zhang
Abstract
Xiao Mei Luo, Wenjing Du, Hao Li, Peiyu Li, Chunsheng Ma, Shucai Xu, Jinhuan Zhang
Abstract
Occupant restraint systems are developed based on some baseline experiments. While these experiments can only represent small part of various accident modes, the current procedure for utilizing the restraint systems may not provide the optimum protection in the majority of accident modes. This study presents an approach to predict occupant injury responses before the collision happens, so that the occupant restraint system, equipped with a motorized pretensioner, can be adjusted to the optimal parameters aiming at the imminent vehicle-to-vehicle frontal crash. The approach in this study takes advantage of the information from pre-crash systems, such as the time to collision, the relative velocity, the frontal overlap, the size of the vehicle in the front and so on. In this paper, the vehicle containing these pre-crash features will be referred to as ego vehicle. The information acquired and the basic crash test results can be integrated to predict a simplified crash pulse. The injury of the occupant in the ego vehicle can thus be predicted using this crash pulse. The approach is verified by some vehicle-to-vehicle crash tests.
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Occupant restraint systems are developed based on some baseline experiments. While these experiments can only represent small part of various accident modes, the current procedure for utilizing the restraint systems may not provide the optimum protection in the majority of accident modes. This study presents an approach to predict occupant injury responses before the collision happens, so that the occupant restraint system, equipped with a motorized pretensioner, can be adjusted to the optimal parameters aiming at the imminent vehicle-to-vehicle frontal crash. The approach in this study takes advantage of the information from pre-crash systems, such as the time to collision, the relative velocity, the frontal overlap, the size of the vehicle in the front and so on. In this paper, the vehicle containing these pre-crash features will be referred to as ego vehicle. The information acquired and the basic crash test results can be integrated to predict a simplified crash pulse. The injury of the occupant in the ego vehicle can thus be predicted using this crash pulse. The approach is verified by some vehicle-to-vehicle crash tests.
Key concepts: Crash, Computer science, Programming language