用对抗智能体生成极端驾驶场景，提升自动驾驶安全测试效果。
Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario
- 引入红队智能体主动干扰自动驾驶车辆，挖掘罕见危险场景。
- 实验验证该框架能显著影响自动驾驶决策安全并生成多样化极限案例。
- 适合自动驾驶安全测试与强化学习鲁棒性研究者参考。
当前针对安全关键场景的决策研究多依赖低效的数据驱动场景生成或特定建模方法，难以捕捉真实世界中的边缘情况。为此，我们提出一种红队多智能体强化学习框架，将具备干扰能力的背景车辆视为红队智能体。通过主动干扰与探索，红队车辆可发现超出数据分布的边缘案例。框架采用约束图表示的马尔可夫决策过程，确保红队车辆遵守安全规则的同时持续干扰自动驾驶车辆（AV）。构建策略威胁区域模型以量化红队车辆对AV的威胁，诱导更极端行为，提高场景危险程度。实验结果表明，该框架显著影响自动驾驶决策安全性，并生成多种边缘案例。该方法为安全关键场景研究提供了新方向。
原文摘要 · Abstract (English)
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.
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