Uncertainty-Aware Collision Avoidance through Safe Reinforcement Learning

Proposed a constrained MDP framework using lagrange PPO and behavior cloning for navigating unsignalized intersections under uncertainty. The method achieves a 100% success rate and zero-shot generalization to out-of-distribution agent behaviors (e.g., zigzagging), validated via high-fidelity simulation and hardware-in-the-loop experiments on Jackal robots.