Sparse Threats, Focused Defense: Criticality-Aware Robust Reinforcement Learning for Safe Autonomous Driving

This paper presents a criticality-aware robust reinforcement learning framework to enhance safety and robustness in autonomous driving systems. By focusing on sparse but critical threats, the method improves the AI's ability to handle unexpected and dangerous situations, leading to more reliable and secure autonomous vehicles in complex real-world scenarios.