用于智能驾驶系统评价的乘员损伤模型
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摘要
只有较少的交通事故数据资源被用于建立基于碰撞速度信息的乘员损伤模型,致使所得到的模型精度差。为此,提出了基于车辆变形深度的乘员损伤模型。对美国不同制造年代和车辆级别的事故数据进行聚类分析,论证出车辆变形深度与乘员损伤风险具有相关性。以车辆变形深度为自变量,通过回归分析得到乘员损伤模型。不同种类车辆的乘员损伤模型拟合精度R~2约为0.9,证明了该模型的正确性。为进一步验证,以此模型为基础,评价智能驾驶系统的有效性。以自动紧急制动系统为例,对比基于变形深度和速度变化量信息2种方法的有效性计算结果。结果表明:2组结果的平均误差不超过1%,验证了基于变形深度的乘员损伤模型的准确性。该模型仅需要事故数据库中准确的变形深度信息,能够获得更多的事故数据支持,从而可以更好地适应于不同类别智能驾驶系统的评价需求。
The occupant injury model is often characterized as a function of the velocity information in an accident. Only a small amount of the total traffic accident data resources is used to fit the occupant injury model, which results in the poor accuracy of the obtained model. An occupant injury model based on the vehicle deformation depth is presented in this paper. A cluster analysis of vehicles of different ages and grades is carried out with accident data from the US to demonstrate the interdependency between the vehicle deformation depth and the occupant injury risk. Considering the vehicle deformation depth as an independent variable, the occupant injury model is obtained by the regression analysis. The accuracy R~2 of the fitted injury models for different types of vehicles is about 0.9, which indicates the reliability of the model. This paper puts forward a safety benefit evaluation method of the intelligent driving system based on the injury model. An automatic emergency braking system is evaluated using the deformation depth and velocity information methods. The difference between the two results is below 1%, which verifies the accuracy of the injury model. The proposed model needs accurate deformation depth information in the accident database. Further, it can get more accident data support and can be better adapted to evaluation needs of various intelligent driving systems.
The occupant injury model is often characterized as a function of the velocity information in an accident. Only a small amount of the total traffic accident data resources is used to fit the occupant injury model, which results in the poor accuracy of the obtained model. An occupant injury model based on the vehicle deformation depth is presented in this paper. A cluster analysis of vehicles of different ages and grades is carried out with accident data from the US to demonstrate the interdependency between the vehicle deformation depth and the occupant injury risk. Considering the vehicle deformation depth as an independent variable, the occupant injury model is obtained by the regression analysis. The accuracy R~2 of the fitted injury models for different types of vehicles is about 0.9, which indicates the reliability of the model. This paper puts forward a safety benefit evaluation method of the intelligent driving system based on the injury model. An automatic emergency braking system is evaluated using the deformation depth and velocity information methods. The difference between the two results is below 1%, which verifies the accuracy of the injury model. The proposed model needs accurate deformation depth information in the accident database. Further, it can get more accident data support and can be better adapted to evaluation needs of various intelligent driving systems.
引文
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[16] 杨娜,张臻,赵桂范,等.自动制动系统行人保护效果的研究[J].汽车工程,2015,37(12):1400-1405,1394.YANG Na,ZHANG Zhen,ZHAO Gui-fan,et al.A Research on the Pedestrian Protection Effects of Automatic Braking System [J].Automotive Engineering,2015,37 (12):1400-1405,1394.
[17] 乔希永,张国胜,王成虎,等.基于车辆-护栏碰撞的乘员风险评价方法[J].公路交通科技,2013,30(4):155-158.QIAO Xi-yong,ZHANG Guo-sheng,WANG Cheng-hu,et al.A Method of Occupant Risk Evaluation Based on Vehicle-guardrail Impact [J].Journal of Highway and Transportation Research and Development,2013,30 (4):155-158.
[18] BEDARD M,GUYATT G H,STONES M J,et al.The Independent Contribution of Driver,Crash,and Vehicle Characteristics to Driver Fatalities [J].Accident Analysis & Prevention,2002,34 (6):717-727.
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[20] 陈龙,ZOBEL R,李克强,等.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,ZOBEL R,LI Ke-qiang,et al.Method to Evaluate Safety Enhancement of the Past and Methodology to Predict Future Enhancement Based on Deformation Depth [J].Journal of Tsinghua University:Science and Technology,2016,56 (2):124-129.
[21] BRAVER E R,TREMPEL R E.Are Older Drivers Actually at Higher Risk of Involvement in Collisions Resulting in Deaths or Non-fatal Injuries Among Their Passengers and Other Road Users?[J].Injury Prevention,2004,10 (1):27-32.
[22] 何晓群.多元统计分析 [M].4版.北京:中国人民大学出版社,2015.HE Xiao-qun.Multivariate Statistical Analysis [M].4th ed.Beijing:China Renmin University Press,2015.
[23] VANGI D,BEGANI F.Energy Loss in Vehicle Collisions from Permanent Deformation:An Extension of the ‘Triangle Method’ [J].Vehicle System Dynamics,2013,51 (6):857-876.
[2] 刘锴,贾洁,刘超,等.车路协同环境下道路无信号交叉口防碰撞系统警示效果[J].中国公路学报,2018,31(4):222-230.LIU Kai,JIA Jie,LIU Chao,et al.Warning Effectiveness of Vehicle-to-infrastructure Cooperative Crossing Collision Prevention System at Non-signal Controlled Intersection [J].China Journal of Highway and Transport,2018,31 (4):222-230.
[3] ZELLNER J W,VAN AUKEN R M,CHIANG D P,et al.Extension of the Honda-DRI “Safety Impact Methodology” (SIM) for the NHTSA Advanced Crash Avoidance Technology (ACAT) Program and Application to a Prototype Advanced Collision Mitigation Braking System [J].SAE International Journal of Passenger Cars - Mechanical Systems,2009,2 (1):875-894.
[4] BADEA-ROMERO A,PAEZ F J,FURONES A,et al.Assessing the Benefit of the Brake Assist System for Pedestrian Injury Mitigation Through Real-world Accident Investigations [J].Safety Science,2013,53:193-201.
[5] APARICIA A,LESEMANN M,ERIKSSON H.Status of Test Methods for Autonomous Emergency Braking Systems - Results from the Active Test Project [J].SAE Paper 2013-01-0725.
[6] 中国汽车工程学会.节能与新能源汽车技术路线图[M].北京:机械工业出版社,2016.Society of Automotive Engineers of China.Technology Roadmap for Energy Saving and New Energy Vehicles [M].Beijing:China Machine Press,2016.
[7] EDWARDS M,NATHANSON A,CARROLL J,et al.Assessment of Integrated Pedestrian Protection Systems with Autonomous Emergency Braking (AEB) and Passive Safety Components [J].Traffic Injury Prevention,2015,16 (S1):S2-S11.
[8] KUSANO K D,GABLER H C.Safety Benefits of Forward Collision Warning,Brake Assist,and Autonomous Braking Systems in Rear-end Collisions [J].IEEE Transactions on Intelligent Transportation Systems,2012,13 (4):1546-1555.
[9] RIEXINGER L E,SHERONY R,GABLER H C.Methodology for Estimating the Benefits of Lane Departure Warnings using Event Data Recorders [J].SAE Paper 2018-01-0509.
[10] BAHOUTH G T,DIGGES K H,BEDEWI N E,et al.Development of URGENCY 2.1 for the Prediction of Crash Injury Severity [J].Topics in Emergency Medicine,2004,26 (2):157-165.
[11] YUAN Quan,HU Meng-xia,LI Yi-bing.Scenario Design and Driving Simulation Experiment of Vehicle-to-pedestrian Accidents Based on Real Accident Data [J].Journal of Automotive Safety and Energy,2012,3 (1):19-25.
[12] 代超.行人避撞安全系统的关键参数及效用评价研究[D].上海:同济大学,2015.DAI Chao.Research on the Key Parameters and Effectiveness Evaluation of the Pedestrian Collision-avoided Safety System [D].Shanghai:Tongji University,2015.
[13] 褚端峰,吴超仲,李顺喜,等.基于Logistic回归的高速公路车-车碰撞事故深度分析[J].中国安全科学学报,2014,24(3):103-108.CHU Duan-feng,WU Chao-zhong,LI Shun-xi,et al.In-depth Analysis of Vehicle-vehicle Crash on Freeways Based on Logistic Regression [J].China Safety Science Journal,2014,24 (3):103-108.
[14] 胡林,方胜勇,黄晶,等.基于逻辑回归的二轮车-汽车碰撞事故深度分析[J].汽车工程,2016,38(11):1288-1293.HU Lin,FANG Sheng-yong,HUANG Jing,et al.In-depth Analysis on Cycle-vehicle Crash Accident Based on Logistic Regression [J].Automotive Engineering,2016,38 (11):1288-1293.
[15] 何鋆,郭飒飒,苗强,等.基于真实交通工况的自动紧急制动系统安全收益分析[C]//国际汽车交通安全学术会议学术委员会.第11届国际汽车交通安全学术会议论文集.重庆:INFATS,2014:379-386.HE Jun,GUO Sa-sa,MIAO Qiang,et al.A Safety Impact Benefit Assessment of Autonomous Emergency Braking Systems Based on Real Traffic Scenarios[C] // Academic Committee of INFATS.Proceedings of the 11th International Forum of Automotive Traffic Safety.Chongqing:INFATS,2014:379-386.
[16] 杨娜,张臻,赵桂范,等.自动制动系统行人保护效果的研究[J].汽车工程,2015,37(12):1400-1405,1394.YANG Na,ZHANG Zhen,ZHAO Gui-fan,et al.A Research on the Pedestrian Protection Effects of Automatic Braking System [J].Automotive Engineering,2015,37 (12):1400-1405,1394.
[17] 乔希永,张国胜,王成虎,等.基于车辆-护栏碰撞的乘员风险评价方法[J].公路交通科技,2013,30(4):155-158.QIAO Xi-yong,ZHANG Guo-sheng,WANG Cheng-hu,et al.A Method of Occupant Risk Evaluation Based on Vehicle-guardrail Impact [J].Journal of Highway and Transportation Research and Development,2013,30 (4):155-158.
[18] BEDARD M,GUYATT G H,STONES M J,et al.The Independent Contribution of Driver,Crash,and Vehicle Characteristics to Driver Fatalities [J].Accident Analysis & Prevention,2002,34 (6):717-727.
[19] FUNK J R,CORMIER J M,GABLER H C.Effect of Delta-V Errors in NASS on Frontal Crash Risk Calculations [J].Annals of Advances in Automotive Medicine,2008,52:155-164.
[20] 陈龙,ZOBEL R,李克强,等.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,ZOBEL R,LI Ke-qiang,et al.Method to Evaluate Safety Enhancement of the Past and Methodology to Predict Future Enhancement Based on Deformation Depth [J].Journal of Tsinghua University:Science and Technology,2016,56 (2):124-129.
[21] BRAVER E R,TREMPEL R E.Are Older Drivers Actually at Higher Risk of Involvement in Collisions Resulting in Deaths or Non-fatal Injuries Among Their Passengers and Other Road Users?[J].Injury Prevention,2004,10 (1):27-32.
[22] 何晓群.多元统计分析 [M].4版.北京:中国人民大学出版社,2015.HE Xiao-qun.Multivariate Statistical Analysis [M].4th ed.Beijing:China Renmin University Press,2015.
[23] VANGI D,BEGANI F.Energy Loss in Vehicle Collisions from Permanent Deformation:An Extension of the ‘Triangle Method’ [J].Vehicle System Dynamics,2013,51 (6):857-876.