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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.15587 |
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| _version_ | 1866912494626275328 |
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| author | Chen, Yinsong Wang, Kaifeng Meng, Xiaoqiang Li, Xueyuan Li, Zirui Gao, Xin |
| author_facet | Chen, Yinsong Wang, Kaifeng Meng, Xiaoqiang Li, Xueyuan Li, Zirui Gao, Xin |
| contents | Current research on decision-making in safety-critical scenarios often relies on inefficient data-driven scenario generation or specific modeling approaches, which fail to capture corner cases in real-world contexts. To address this issue, we propose a Red-Team Multi-Agent Reinforcement Learning framework, where background vehicles with interference capabilities are treated as red-team agents. Through active interference and exploration, red-team vehicles can uncover corner cases outside the data distribution. The framework uses a Constraint Graph Representation Markov Decision Process, ensuring that red-team vehicles comply with safety rules while continuously disrupting the autonomous vehicles (AVs). A policy threat zone model is constructed to quantify the threat posed by red-team vehicles to AVs, inducing more extreme actions to increase the danger level of the scenario. Experimental results show that the proposed framework significantly impacts AVs decision-making safety and generates various corner cases. This method also offers a novel direction for research in safety-critical scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15587 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario Chen, Yinsong Wang, Kaifeng Meng, Xiaoqiang Li, Xueyuan Li, Zirui Gao, Xin Machine Learning Artificial Intelligence Current research on decision-making in safety-critical scenarios often relies on inefficient data-driven scenario generation or specific modeling approaches, which fail to capture corner cases in real-world contexts. To address this issue, we propose a Red-Team Multi-Agent Reinforcement Learning framework, where background vehicles with interference capabilities are treated as red-team agents. Through active interference and exploration, red-team vehicles can uncover corner cases outside the data distribution. The framework uses a Constraint Graph Representation Markov Decision Process, ensuring that red-team vehicles comply with safety rules while continuously disrupting the autonomous vehicles (AVs). A policy threat zone model is constructed to quantify the threat posed by red-team vehicles to AVs, inducing more extreme actions to increase the danger level of the scenario. Experimental results show that the proposed framework significantly impacts AVs decision-making safety and generates various corner cases. This method also offers a novel direction for research in safety-critical scenarios. |
| title | Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2507.15587 |