汽车工程 ›› 2018, Vol. 40 ›› Issue (9): 1048-1053.doi: 10.19562/j.chinasae.qcgc.2018.09.007

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基于环境态势评估的智能车自主变道决策机制*

何艳侠,尹慧琳,夏鹏飞   

  1. 同济大学中德学院电子信息系,上海 200092
  • 收稿日期:2017-08-07 出版日期:2018-09-25 发布日期:2018-09-25
  • 通讯作者: 尹慧琳,教授,E-mail:yinhuilin@tongji.edu.cn
  • 基金资助:
    科技部国家重点研发计划新能源汽车专项(2016YFB0100901)和TUEV SUED基金(20162020)资助

Decision-making Mechanism of Autonomous Lane-change for
Intelligent Vehicles Based on Environment Situation Assessment

He Yanxia, Yin Huilin & Xia Pengfei   

  1. Department of Electronic and Information, Sino-German School, Tongji University, Shanghai 200092
  • Received:2017-08-07 Online:2018-09-25 Published:2018-09-25

摘要: 汽车面对的是复杂高动态的行驶环境,且车载传感器信息具有不确定性,对动态环境进行正确态势评估是提高车辆,尤其是智能车行驶安全性的关键因素之一,本文中基于环境态势评估对智能车自主变道决策机制进行研究。首先基于人类驾驶认知机理对车辆环境态势评估模型进行层次化分析,然后利用动态贝叶斯网络实现态势评估,并结合最大期望效用原则实现自主变道决策,最后通过实验验证了本文方法的有效性。结果表明,该方法能在动态复杂环境和车载传感器测量数据存在偏差等信息不确定的条件下,做出正确合理的变道决策。

关键词: 智能车, 自主变道决策, 态势评估, 动态贝叶斯网络, 最大期望效用

Abstract: Motor vehicles work in the complex and highly dynamic driving environment, with the information uncertainty of onboard sensors, so the correct situation assessment of dynamic environment is one of the key factors to the safety of motor vehicles, in particular, intelligent vehicles. In this paper, the mechanism of autonomous line-change decision-making for intelligent vehicle is studied based on environment situation assessment. Firstly a hierarchy analysis on vehicle environment situation assessment model is conducted based on human driving cognitive mechanism. Then the dynamic Bayesian network is used to fulfill situation assessment, and autonomous line-change decision-making is achieved with the principle of maximum expected utility. Finally, the effectiveness of the method proposed is verified by experiments. The results show that the method proposed can make correct and reasonable decision for lane change under complex dynamic environment and the condition of information uncertainty such as measured data deviation of onboard sensors

Key words: intelligent vehicles, autonomous lane change decision, situation assessment, dynamic Bayesian network, maximum expected utility