Red-Team Multi-Agent Reinforcement Learning for Emergency Braking Scenario
This addresses safety-critical scenario generation for autonomous vehicles, offering a novel direction but appearing incremental in method.
The paper tackles the problem of generating corner cases for autonomous vehicle decision-making in safety-critical scenarios by proposing a Red-Team Multi-Agent Reinforcement Learning framework, where red-team vehicles actively interfere to uncover risks, resulting in significant impacts on safety and generation of various corner cases.
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.