Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning

Reinforcement learning (RL) has shown promising performance in autonomous driving, yet ensuring the safety of online RL policies remains challenging due to insufficient exposure to safety-critical driving scenes. The long-tailed nature of real-world traffic situations makes dangerous and rare interactions difficult to encounter through conventional sampling, limiting the ability of RL policies to learn robust safety behaviors. Existing methods improve training diversity by synthesizing challengi

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ethics.ai (11 August 2026), “Threat-guided Policy-aware Scene Perturbation for Safe Autonomous Driving with Online Reinforcement Learning,” evidence record 18430, https://ethics.ai/record/18430 (originally published by arXiv).

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