Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving

Summary

Researchers present EthicAR, a hierarchical Safe Reinforcement Learning framework for autonomous driving that adds ethics-aware cost signals combining collision probability and harm severity. The method adds risk-sensitive prioritized experience replay and Temporal Cost Aggregation, which aligns learning with tail-risk measures such as Conditional Value-at-Risk. In closed-loop simulations on the Waymo Open Dataset across 75 scenarios and five random seeds, it reduced collision rates by 20 to 45% versus baselines while keeping task success and comfort within 5 to 10% of baselines.