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A probabilistic risk assessment framework considering lane-changing behavior interaction

Science China(Information Sciences) 2020年第9期63卷 94-108页

作者： Heye HUANG Jianqiang WANG Cong FEI Xunjia ZHENG Yibin YANG Jinxin LIU Xiangbin WU Qing XU The State Key Laboratory of Automotive Safety and Energy Tsinghua University Intel

Understanding the dynamic characteristics of surrounding vehicles and estimating the potential risk of mixed traffic can help reliable autonomous driving. However, the existing risk assessment methods are challenging to detect dangerous situations in advance and tackle the uncertainty of mixed traffic. In this paper, we propose a probabilistic driving risk assessment framework based on intention identification and risk assessment of surrounding vehicles. Firstly, we set up an intention identification model(IIM) via long short-term memory(LSTM) networks to identify the intention possibility of the surrounding vehicles. Then a risk assessment model(RAM) based on the driving safety field is employed to output the potential ***, driving safety field can reflect the coupling relationship of drivers, vehicles, and roads by analyzing their interaction. Finally, an integrated risk evaluation model combining both IIM and RAM is developed to form a dynamic potential risk map considering multi-vehicle interaction. For example, in a typical but challenging lane-changing scenario, an intelligent vehicle can assess its driving status by calculating a risk map in real time that represents the risk generated by the estimated intentions of surrounding ***, simulations and naturalistic driving experiments are conducted in the extracted lane-changing scenarios, and the results verify the effectiveness of the proposed model considering lane-changing behavior interaction.

关键词： behavior probability risk assessment intention identification LSTM driving safety field

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