Method to evaluate safety enhancement of the past and methodology to predict future enhancement based on vehicle deformation depth
CHEN Long1, Robert Zobel1,2, LI Keqiang1, WANG Hongyan2, CHEN Junyi2
1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;
2. School of Automotive Engineering, Tongji University, Shanghai 200092, China
Abstract：Accident velocity information is difficult to obtain with accident reconstruction used to compute velocities, primarily using energy-based estimates. However, the different stiffnesses of the vehicles are difficult to estimate, which leads to significant uncertainty in the computed velocity. This is a major problem because most approaches for estimating the effect of driver assistant systems need the velocities of the involved vehicles. A method is given here to evaluate past safety enhancements to predict future enhancement based on the vehicle deformation depth. Analyses of German accident data show that for vehicles of significantly different stiffnesses, the vehicle deformation depth more truly reflects the severity of the accident than the velocity change. Vehicle safety enhancements over the past 30 years are then calculated based on the deformation depth. The results are in good agreement with historical data, which verifies the safety enhancement evaluation method using deformation depth information. A deformation-depth-based safety impact prediction method is also given for driver assistance systems. This prediction method uses the accurate deformation depth information in the accident database to improve evaluations of driver assistance systems.
陈龙, Robert Zobel, 李克强, 王宏雁, 陈君毅. 基于车辆变形深度的汽车安全有效性的评价与预测方法[J]. 清华大学学报（自然科学版）, 2016, 56(2): 124-129.
CHEN Long, Robert Zobel, LI Keqiang, WANG Hongyan, CHEN Junyi. Method to evaluate safety enhancement of the past and methodology to predict future enhancement based on vehicle deformation depth. Journal of Tsinghua University(Science and Technology), 2016, 56(2): 124-129.
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2016, Vol. 56 
