轿车与行人事故重建的参数敏感性研究
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摘要
为确定轿车与行人事故再现系统中各参数的敏感性,从PC-Crash软件中提取9个参数,设计正交试验,运用灰色关联度分析方法,分别获得影响轿车、行人停止位置的参数敏感性排序,按是否使用敏感性排序结果,对一起真实人车碰撞事故进行2次重建,验证试验结果的可信性与可靠性。结果表明:对再现结果有显著影响的参数为轿车碰撞时车速、行人速度、碰撞时轿车方向盘转向角、碰撞时轿车行驶方向和人体与地面摩擦因数;针对性地调整这些参数,能够明显降低各参与方的最终位置误差,并提升人车事故重建速度和与实际事故的拟合度。
In order to determine the sensitivity of each parameter in the car and pedestrian accident reconstruction system,nine parameters were extracted by PC-Crash software,and the orthogonal test was designed. The sensitivity ranking of parameters affecting the stop position of car and pedestrian was obtained by grey relational analysis,and according to whether the ranking results are used or not,a real carpedestrian collision accident were reconstructed twice to verify the credibility and reliability of test results.The results show that parameters that have a significant impact on the reconstruction results are the vehicle speed,pedestrian speed,steering angle of car steering wheel during collision,the direction of the car in collision,and the friction coefficient between human body and the ground,and that by adjusting these parameters in a targeted manner,the final position error of each participant can be significantly reduced,and the speed of accident reconstruction and its fitting degree with the actual accident can be improved.
In order to determine the sensitivity of each parameter in the car and pedestrian accident reconstruction system,nine parameters were extracted by PC-Crash software,and the orthogonal test was designed. The sensitivity ranking of parameters affecting the stop position of car and pedestrian was obtained by grey relational analysis,and according to whether the ranking results are used or not,a real carpedestrian collision accident were reconstructed twice to verify the credibility and reliability of test results.The results show that parameters that have a significant impact on the reconstruction results are the vehicle speed,pedestrian speed,steering angle of car steering wheel during collision,the direction of the car in collision,and the friction coefficient between human body and the ground,and that by adjusting these parameters in a targeted manner,the final position error of each participant can be significantly reduced,and the speed of accident reconstruction and its fitting degree with the actual accident can be improved.
引文
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[12]刘洋,何晓聪,邢保英,等.基于灰色理论和神经网络的自冲铆接头力学性能预测[J].塑性工程学报,2017,24(4):71-76.LIU Yang,HE Xiaocong,XING Baoying,et al. Prediction of mechanical property of self-piercing riveted joints based on grey theory and neural network[J]. Journal of Plasticity Engineering,2017,24(4):71-76.
[2] CHEN Qiang,LIN Miao,DAI Bing,et al. Typical pedestrian accident scenarios in China and crash severity mitigation by autonomous emergency braking systems[C]. SAE Paper,2015-01-1464.
[3]邹铁方,易亮,尹若愚,等. Pc-Crash中2种车辆侧滑事故仿真方法对比研究[J].中国安全科学学报,2016,26(7):80-84.ZOU Tiefang,YI Liang,YIN Ruoyu,et al. Comparative study of two simulation methods for reconstructing side-slip accident in Pc-Crash[J]. China Safety Science Journal,2016,26(7):80-84.
[4]邹铁方,张勇刚,陈元新.基于Pc-Crash的车辆侧滑事故再现方法[J].中国安全科学学报,2013,23(1):77-82.ZOU Tiefang,ZHANG Yonggang,CHEN Yuanxin. A method for reconstructing vehicle sideslip accidents based on Pc-Crash[J]. China Safety Science Journal,2013,23(1):77-82.
[5]胡林,戴兴兴,黄晶,等.轿车-行人碰撞事故再现模型参数敏感性研究[J].汽车工程,2017,39(2):159-167.HU Lin,DAI Xingxing,HUANG Jing,et al. A study on parameter sensitivity in accident reconstruction model for carpedestrian crash[J]. Automotive Engineering,2017,39(2):159-167.
[6]刘文君.基于有限元仿真和遗传神经网络的轿车-行人碰撞再现研究[D].重庆:第三军医大学,2015.LIU Wenjun. Car-pedestrian collision reconstruction on the basis of finite element simulation and genetic neural network[D]. Chongqing:Third Military Medical University,2015.
[7] HAN I. Impulse-momentum based analysis of vehicle collision accidents using Monte Carlo simulation methods[J].International Journal of Automotive Technology,2015,16(2):253-270.
[8]王宏雁,邵文煜.基于Pc-crash的交通事故再现误差分析[J].同济大学学报:自然科学版,2009,37(4):531-536.WANG Hongyan,SHAO Wenyu. Error analysis of traffic accident reconstruction based on Pc-crash[J]. Journal of Tongji University:Natural Science,2009,37(4):531-536.
[9]张晓云,金先龙,柴象海,等.基于接触特征的车—人碰撞事故模型构建与分析[J].机械工程学报,2012,48(14):104-111.ZHANG Xiaoyun,JIN Xianlong,CHAI Xianghai,et al. Modeling and analysis method of vehicle-pedestrian crash accidents based on contact characters[J]. Journal of Mechanical Engineering,2012,48(14):104-111.
[10]李威,钱宇彬,冯浩.轿车-电动两轮车事故再现的参数敏感性研究[J].中国安全科学学报,2018,28(5):49-55.LI Wei,QIAN Yubin,FENG Hao. Study on sensitivities of reconstruction system to parameters for collision between sedan and electric two-wheeler[J]. China Safety Science Journal,2018,28(5):49-55.
[11]胡林,方胜勇,陈强.基于正交实验的汽车-两轮车碰撞事故再现的参数影响研究[J].汽车工程,2016,38(5):567-573.HU Lin,FANG Shengyong,CHEN Qiang. A research on the parameter influences of the accident reconstruction for a car-motorcycle collision based on orthogonal experiments[J]. Automotive Engineering,2016,38(5):567-573.
[12]刘洋,何晓聪,邢保英,等.基于灰色理论和神经网络的自冲铆接头力学性能预测[J].塑性工程学报,2017,24(4):71-76.LIU Yang,HE Xiaocong,XING Baoying,et al. Prediction of mechanical property of self-piercing riveted joints based on grey theory and neural network[J]. Journal of Plasticity Engineering,2017,24(4):71-76.