Extended driving risk field model for i-VICS and its application
TIAN Ye1, PEI Huaxin1, YAN Song1, ZHANG Yi1,2,3
1. Department of Automation, Tsinghua University, Beijing 100084, China; 2. Tsinghua-Berkeley Shenzhen Institute(TBSI), Shenzhen 518055, China; 3. Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 210096, China
Abstract：In intelligent vehicle-infrastructure cooperation systems (i-VICS), the driving risk field is an effective method for evaluating the driving safety of connected and automated vehicles (CAVs). However, existing driving risk field models do not consider the geometric characteristics and heading angles of vehicles and ignore the influences of the ego vehicle, which limits the accuracy of these existing models for vehicle safety assessments. This paper describes an extended driving risk field model. This driving risk field model includes the time to collision (TTC) and adds the physical attributes and movement information of the ego vehicle, including the vehicle size and heading, into the driving risk field model which improves the safety assessment. Application of this driving risk field model to typical traffic scenarios shows that this extended model overcomes the limitations of existing models. Simulations using this model for trajectory planning demonstrate the promising performance of the extended model.
田野, 裴华鑫, 晏松, 张毅. 车路协同环境下行车风险场模型的扩展与应用[J]. 清华大学学报（自然科学版）, 2022, 62(3): 447-457.
TIAN Ye, PEI Huaxin, YAN Song, ZHANG Yi. Extended driving risk field model for i-VICS and its application. Journal of Tsinghua University(Science and Technology), 2022, 62(3): 447-457.
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2022, Vol. 62 
