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A probabilistic risk assessment framework considering lane-changing behavior interaction

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Abstract

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 risk. Specifically, 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 vehicles. Furthermore, 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.

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Acknowledgements

This work was supported by the Major Project of National Natural Science Foundation of China (Grant No. 61790561), National Science Fund for Distinguished Young Scholars (Grant No. 51625503), Intel Collaborative Research Institute on Intelligent and Automated Connected Vehicles (ICRI-IACV), the Joint Laboratory for Internet of Vehicle, and Ministry of Education — China Mobile Communications Corporation. We would also like to express our great thanks to the Ph.D. candidates, Hui XIONG and Yang LI, who participated in the discussion and optimized the study.

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Authors and Affiliations

  1. The State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, 100084, China

    Heye Huang, Jianqiang Wang, Cong Fei, Xunjia Zheng, Yibin Yang, Jinxin Liu & Qing Xu

  2. Intel, Beijing, 100084, China

    Xiangbin Wu

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  1. Heye Huang
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  2. Jianqiang Wang
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  3. Cong Fei
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  4. Xunjia Zheng
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  6. Jinxin Liu
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  7. Xiangbin Wu
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  8. Qing Xu
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Corresponding authors

Correspondence to Xunjia Zheng or Qing Xu.

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Huang, H., Wang, J., Fei, C. et al. A probabilistic risk assessment framework considering lane-changing behavior interaction. Sci. China Inf. Sci. 63, 190203 (2020). https://doi.org/10.1007/s11432-019-2983-0

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  • DOI: https://doi.org/10.1007/s11432-019-2983-0

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