汽车工程

汽车工程 ›› 2019, Vol. 41 ›› Issue (2): 153-160.doi: 10.19562/j.chinasae.qcgc.2019.02.006

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基于驾驶员避撞行为的行车风险判别方法的仿真研究*

熊晓夏, 陈龙, 梁军, 蔡英凤, 江浩斌   

  1. 江苏大学汽车与交通工程学院,镇江 212013
  • 收稿日期:2017-12-11 出版日期:2019-02-25 发布日期:2019-02-25
  • 通讯作者: 陈龙,教授,博士,E-mail:chenlong@ujs.edu.cn
  • 基金资助:
    国家自然科学基金(U1564201和61773184)、中国博士后科学基金(2016M600375)和江苏省“六大人才高峰”项目(2015-DZXX-048)资助。

Simulation Study on Driving Risk Discrimination Based on Driver's Collision Avoidance Behavior

Xiong Xiaoxia, Chen Long, Liang Jun, Cai Yingfeng, Jiang Haobin   

  1. School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013
  • Received:2017-12-11 Online:2019-02-25 Published:2019-02-25

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摘要： 提出了一种基于驾驶员避撞行为的行车风险状态分类方法,并综合考虑驾驶员驾驶行为、道路和环境因素对行车风险状态变化的影响,运用支持向量机(SVM)建立不同行车模式下行车风险判别算法。基于美国弗吉尼亚理工大学“100-car”自然驾驶数据对预测算法进行了训练和验证,结果表明,在进行行车风险状态预测建模时考虑驾驶员行为、道路和环境因素的差异(特别是驾驶员分心状态)将有利于提高预测模型的准确率;另外,在满足假正率低于5%的条件下,本文构建的预测算法对未来行车过程中的高风险状态预测具有较高的准确率,有助于对临近危险状态的驾驶员给予及时的警告或辅助纠正,为防撞预警策略和控制方法的研究提供了新的思路。

关键词: 防碰撞预警, 行车风险判别, 支持向量机, 驾驶辅助系统

Abstract: A driving risk classification method based on driver's collision avoidance behavior is proposed, and the driving risk discrimination algorithms under different driving modes are established by using support vector machine with concurrent consideration of the effects of driving behavior, road condition and environmental factor on driving risk states. Training and validation on prediction algorithm are conducted based on the “100-car” natural driving data from Virginia Tech in the US. The results show that in driving risk prediction modeling, the consideration of the discrepancies in driver's behavior, road condition and environmental factor, in particular the driver's distraction state, is conducive to increasing the accuracy of prediction model. In addition, under the condition of false positive rate lower than 5%, the prediction of high-risk state for future driving process by using prediction algorithm created has higher accuracy, in favor of giving timely warning or correction aids to drivers in near danger state, providing a new idea for the research on collision avoidance warning strategy and control method

Key words: collision warning, driving risk discrimination, SVM, driving assistance system