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Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles

文献类型：期刊论文

作者Zheng, Xiaokun1,4; Liang, Huawei1,2,3; Wang, Jian1,4; Wang, Hanqi1,4
刊名MACHINES
出版日期2024-08-01
卷号12
关键词autonomous vehicles critical scenario generation game theory adversarial interaction testing efficiency
DOI10.3390/machines12080538
通讯作者Zheng, Xiaokun(zhengkun@mail.ustc.edu.cn)
英文摘要Ensuring safety and efficiency in the rapidly advancing autonomous vehicle (AV) industry presents a significant engineering challenge. Comprehensive performance evaluations and critical scenario testing are essential for identifying situations that AVs cannot handle. Thus, generating critical scenarios is a key problem in AV testing system design. This paper proposes a game-theoretic adversarial interaction method to efficiently generate critical scenarios that challenge AV systems. Initial motion prediction for adversarial and surrounding vehicles is based on kinematic models and road constraints, establishing interaction action spaces to determine possible driving domains. A novel evaluation approach combines reachability sets with adversarial intensity to assess collision risks and adversarial strength for any state, used to solve behavior values for each interaction action state. Further, equilibrium action strategies for the vehicles are derived using Stackelberg game theory, yielding optimal actions considering adversarial interactions in the current traffic environment. Simulation results show that the adversarial scenarios generated by this method significantly increase incident rates by 158% to 1313% compared to natural driving scenarios, while ride comfort and driving efficiency decrease, and risk significantly increases. These findings provide critical insights for model improvement and demonstrate the proposed method's suitability for assessing AV performance in dynamic traffic environments.
WOS关键词ATTACK
资助项目National Key Research and Development Program of China ; [2020AAA0108103]
WOS研究方向Engineering
语种英语
WOS记录号WOS:001305809800001
出版者MDPI
资助机构National Key Research and Development Program of China
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/134993]  
专题中国科学院合肥物质科学研究院
通讯作者Zheng, Xiaokun
作者单位1.Chinese Acad Sci, Hefei Inst Phys Sci, Hefei 230031, Peoples R China
2.Anhui Engn Lab Intelligent Driving Technol & Appli, Hefei 230031, Peoples R China
3.Chinese Acad Sci, Innovat Res Inst Robot & Intelligent Mfg, Hefei 230031, Peoples R China
4.Univ Sci & Technol China, Hefei 230026, Peoples R China
推荐引用方式
GB/T 7714		 Zheng, Xiaokun,Liang, Huawei,Wang, Jian,et al. Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles[J]. MACHINES,2024,12.
APA Zheng, Xiaokun,Liang, Huawei,Wang, Jian,&Wang, Hanqi.(2024).Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles.MACHINES,12.
MLA Zheng, Xiaokun,et al."Game-Theoretic Adversarial Interaction-Based Critical Scenario Generation for Autonomous Vehicles".MACHINES 12(2024).

入库方式： OAI收割

来源：合肥物质科学研究院

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