考虑路面附着因数的车辆向前碰撞预警时间的优化算法
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摘要
为有效避免碰撞事故,提高向前碰撞预警时间(CPWT)的准确率和通行效率,提出一种考虑路面附着因数和车辆行驶状态的即碰时间(TTC)预警方法。利用改进双指数模型,获取不同路面状况下车辆附着系数;根据前车分别处于静止、匀速、匀加速和变加速直线运动等不同行驶状态,构建了对应的考虑路面附着因数的向前CPWT模型;利用Matlab/Simulink软件进行仿真。用该算法分析了前车和跟驰车辆处于不同行驶状态下,积雪路面、潮湿路面、干燥路面的CPWT。结果表明:与其他预警方法相比,该CPWT方法对车辆碰撞预警更加合理有效。因此,该方法可为自动驾驶和车辆预警提供理论支撑。
A time to collision(TTC) warning method was proposed considering road adhesion factor and vehicle driving state to effectively avoid collision accidents and improve the accuracy and efficiency of forward collision pre-warning time(CPWT). An improved dual-exponential model was used to obtain the vehicle adhesion coefficient under different road conditions. A CPWT model was constructed considering road adhesion factor, according to the different driving states of the front vehicle, such as static, uniform speed, uniform acceleration, and variable acceleration linear motion. The CPWTs of car-following forward were simulated by using MATLAB/Simulink. The CPWT algorithm was used to analyze the CPWT for the front vehicle and the following vehicle driving on the snowy roads, wet roads and dry roads and in different driving states. The results show that the CPWT model is more reasonable and effective compared with other collision pre-warning methods. Therefore, the CPWT method can provide a theoretical support for automatic driving and vehicle warning.
A time to collision(TTC) warning method was proposed considering road adhesion factor and vehicle driving state to effectively avoid collision accidents and improve the accuracy and efficiency of forward collision pre-warning time(CPWT). An improved dual-exponential model was used to obtain the vehicle adhesion coefficient under different road conditions. A CPWT model was constructed considering road adhesion factor, according to the different driving states of the front vehicle, such as static, uniform speed, uniform acceleration, and variable acceleration linear motion. The CPWTs of car-following forward were simulated by using MATLAB/Simulink. The CPWT algorithm was used to analyze the CPWT for the front vehicle and the following vehicle driving on the snowy roads, wet roads and dry roads and in different driving states. The results show that the CPWT model is more reasonable and effective compared with other collision pre-warning methods. Therefore, the CPWT method can provide a theoretical support for automatic driving and vehicle warning.
引文
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[2]王畅,付锐,张琼,等.换道预警系统中参数TTC特性研究[J].中国公路学报,2015,28(8):91-100.WANG Chang,FU Rui,ZHANG Qiong,et al.Research on parameter TTC characteristics of lane change waring system[J].Chin J Highway Engng,2015,28(8):91-100,108.(in Chinese)
[3]Hayward J C.Near-miss determination through use of a scale of danger[J].Highway Res Record,1972(384):24-34.
[4]Vogel K.A comparison of headway and time to collision as safety indicators[J].Accident Anal Prevention,2003,35(3):427-433.
[5]Kiefer R J,Leblanc D J,Flannagan C A.Developing an inverse time-to-collision crash alert timing approach based on drivers’last-second braking and steering judgments[J].Accident Analysis Prevention,2005,37(2):295-303.
[6]Balas V E,Balas M M.Driver assisting by inverse time to collision[C]//Word Automation Congress IEEE,2006:1-6
[7]Benamar F Z,Fkihi S E,Demonceaux C,et al.Estimation of time card of collision(TTC)based on the optical flow in para-catadioptric vision[J].Trait Signal,2014,31(1-2):197-219.
[8]朱冰,朴奇,赵健,等.基于路面附着因数估计的汽车纵向碰撞预警策略[J].汽车工程,2016,38(4):449-451.ZHU Bin,PIAO Qi,ZHAO Jian,et al.Vehicle longitudinal collision warning strategy based on road adhesive coefficient estimation[J].Automotive Engng,2016,38(4):449-451.(in Chinese)
[9]WANG Jianqiang,YU Chenfei,LI Shengbo Eben,et al.A forward collision warning algorithm with adaptation to driver behaviors[J].IEEE Trans Int’l Transp Syst,2016,17(4):1157-1167.
[10]刘庆华,邱修林,谢礼猛,等.基于行驶车速的车辆防撞时间预警算法[J].农业工程学报,2017,33(12):102-103.LIU Qinghua,QIU Xiulin,XIE Limeng,et al.Anticollision warning time algorithm based on driving speed of vehicle[J].Trans Chin Soc Agri Engng,2017,33(12):102-103.(in Chinese)
[11]马彬.汽车后向防撞预警系统的研究[D].西安:长安大学,2008.MA Bin.Research on the vehicle’s backside anti-collision warning system[D].Xi’an:Chang’an University,2008.(in Chinese)
[12]余志生.汽车理论(第5版)[M].北京:机械工业出版社,2009:97-100.YU Zhisheng.Automobile Theory(5th Ed)[M].Beijing:China Machine Press,2009:97-100.(in Chinese)
[13]JIN Weidong,LIANG Chunyu,GAO Xuekai,et al.Mechanical effect study of the vehicle braking on the asphalt pavement[J].Appl Mech Mate,2015,744-746:1266-1272.
[14]Pacejka H B,Sharp R S.Shear force developments by pneumatic tires in steady-state conditions:A review of modeling aspects[J].Vehi Syst Dyna,1991,20:121-176.
[15]王博,卢萍萍,管欣,等.路面附着系数识别方法发展现状综述[J].汽车技术,2014(8):1-7.WANG Bo,LU Pingping,GUAN Xin,et al,A review on the development status of road adhesion coefficient identification approach[J].Automobile Technology,2014(8):1-7.(in Chinese)
[16]边明远,李克强.基于双指数模型的纵向附着系数计算模型[J].农业机械学报,2005,36(5):5-8.BIAN Mingyuan,LI Keqiang.Estimation of longitudinal road friction coefficient based on a dual-exponential model[J].Transa Chin Soc Agri Mach,2005,36(5):5-8.(in Chinese)
[17]HOU Yi,Edara P,SUN Carlos.Situation assessment and decision making for lane change assistance using ensemble learning methods[J].Expert Syst Appl,2015,42(8):3875-3882.
[18]郭艳君,徐远新,孙勤英,等.嵌入式换道预警系统设计[J].科学技术与工程,2015,15(18):201-202.GUO Yanjun,XU Yuanxin,SUN Qinying,et al.Embedded design of lane change warning system[J].Sci Tech Engng,2015,15(18):201-202.(in Chinese).