道路交通行车安全预警算法研究
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摘要
为了提高道路交通安全,针对目前行车安全预警算法采用确定性参数导致的预警准确度不高的问题,提出了一种基于多层感知器神经网络的行车安全预警算法。该算法以人工神经网络为基础,将前后车的相对距离、相对速度、驾驶员风格类型、前车加速度、后车加速度以及后车的速度作为系统的输入,行车安全预警级别作为系统的输出。结合样本数据进行训练,得到行车安全预警级别的预测值,并与传统的碰撞时间算法和停车距离算法的两种预警算法进行对比。实验结果表明,多层感知器神经网络预警算法在预警的有效性与准确性方面明显优于传统预警算法。
In order to improve road traffic safety, an early warning algorithm based on Multi-Layer Perceptron Neural Network is proposed to solve the problem of low prediction accuracy caused by deterministic parameters in the current traffic safety early warning algorithm. The algorithm is based on artificial neural network(ANN). The relative distance, relative speed, driver′s driving style, the acceleration of preceding vehicle, the acceleration of following vehicle and the speed of following vehicle are used as the input of the system, and the warning level of traffic safety is the output of the system. The prediction value of traffic safety early warning level is obtained by training with sample data, and compared with the two early warning models of the traditional collision time algorithm and the stop distance algorithm. The experimental results show that the multilayer perceptron neural network early warning algorithm is superior to the traditional warning algorithm in the effectiveness and accuracy of early warning.
In order to improve road traffic safety, an early warning algorithm based on Multi-Layer Perceptron Neural Network is proposed to solve the problem of low prediction accuracy caused by deterministic parameters in the current traffic safety early warning algorithm. The algorithm is based on artificial neural network(ANN). The relative distance, relative speed, driver′s driving style, the acceleration of preceding vehicle, the acceleration of following vehicle and the speed of following vehicle are used as the input of the system, and the warning level of traffic safety is the output of the system. The prediction value of traffic safety early warning level is obtained by training with sample data, and compared with the two early warning models of the traditional collision time algorithm and the stop distance algorithm. The experimental results show that the multilayer perceptron neural network early warning algorithm is superior to the traditional warning algorithm in the effectiveness and accuracy of early warning.
引文
[1]牟涛,王海东,张永刚.基于BP神经网络的高速公路安全车速预测[J].公路,2015,60(2):156-159.
[2]陶鹏飞,王殿海,金盛.车头时距混合分布模型[J].西南交通大学学报,2011,46(4):633-637,644.
[3]吴明.汽车制动距离与速度的关系[J].公路与汽运,2010(3):46-49.
[4]陆建,张文珺,杨海飞,等.基于碰撞时间的追尾风险分析[J].交通信息与安全,2014,32(5):58-64,76.
[5]朱彤,赵永红,白玉,等.基于停车距离的车辆防碰撞系统[J].计算机工程与应用,2009,45(1):13-16.
[6]HAYWARD J C.Near-miss determination through use of a scale of danger[J].Highway Research Record,1972(384):24-34.
[7]Vogel K.A comparison of headway and time to collision as safety indicators.[J].Accident Analysis and Prevention,2003,35(3):427-433.
[8]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.
[9]孟祥海,徐汉清,王浩,等.基于TTC及DRAC的高速公路施工区追尾冲突研究[J].交通信息与安全,2012,30(6):6-10.
[10]强添纲,辛雨蔚,田广东,等.基于广义回归神经网络的车辆制动距离预测[J].森林工程,2014,30(1):73-75.
[11]朱田田.汽车纵向避撞预警算法的设计与仿真实现[D].沈阳:东北大学,2008.
[12]唐阳山,夏道华.驾驶员对汽车防撞安全距离检测仿真研究[J].计算机仿真,2016,33(7):449-453.
[13]徐杰,杜文,孙宏.跟随车安全距离的分析[J].交通运输工程学报,2002(1):101-104.
[14]连晋毅,华小洋.汽车防追尾碰撞数学模型研究[J].中国公路学报,2005(3):123-126.
[15]姜能惠,于建国.基于RBF神经网络的高速公路防追尾模型研究[J].森林工程,2010,26(5):60-62,65.
[2]陶鹏飞,王殿海,金盛.车头时距混合分布模型[J].西南交通大学学报,2011,46(4):633-637,644.
[3]吴明.汽车制动距离与速度的关系[J].公路与汽运,2010(3):46-49.
[4]陆建,张文珺,杨海飞,等.基于碰撞时间的追尾风险分析[J].交通信息与安全,2014,32(5):58-64,76.
[5]朱彤,赵永红,白玉,等.基于停车距离的车辆防碰撞系统[J].计算机工程与应用,2009,45(1):13-16.
[6]HAYWARD J C.Near-miss determination through use of a scale of danger[J].Highway Research Record,1972(384):24-34.
[7]Vogel K.A comparison of headway and time to collision as safety indicators.[J].Accident Analysis and Prevention,2003,35(3):427-433.
[8]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.
[9]孟祥海,徐汉清,王浩,等.基于TTC及DRAC的高速公路施工区追尾冲突研究[J].交通信息与安全,2012,30(6):6-10.
[10]强添纲,辛雨蔚,田广东,等.基于广义回归神经网络的车辆制动距离预测[J].森林工程,2014,30(1):73-75.
[11]朱田田.汽车纵向避撞预警算法的设计与仿真实现[D].沈阳:东北大学,2008.
[12]唐阳山,夏道华.驾驶员对汽车防撞安全距离检测仿真研究[J].计算机仿真,2016,33(7):449-453.
[13]徐杰,杜文,孙宏.跟随车安全距离的分析[J].交通运输工程学报,2002(1):101-104.
[14]连晋毅,华小洋.汽车防追尾碰撞数学模型研究[J].中国公路学报,2005(3):123-126.
[15]姜能惠,于建国.基于RBF神经网络的高速公路防追尾模型研究[J].森林工程,2010,26(5):60-62,65.