A Hierarchical Dynamic Path Planning Framework for Autonomous Vehicles Based on Physics-Informed Potential Field and TD3 Reinforcement Learning

Autonomous driving in dense traffic demands policies that ensure safety, accurate path tracking, and ride comfort, yet reinforcement learning (RL) alone suffers from low sample efficiency and weak safety guarantees, while classical artificial potential field (APF) methods lack adaptability to dynamic scenarios. This paper proposes PIPF-TD3, which integrates APF theory with the Twin Delayed Deep Deterministic Policy Gradient (TD3) by embedding composite potential values and Doppler-weighted gradients as physics-informed features into the state vector. A Hybrid A* planner generates a reference path encoded as an attractive field; repulsive fields model nearby obstacles using real-time perception data; and a multi-objective reward function jointly optimizes path tracking, collision avoidance, and ride comfort. Experiments in CARLA 0.9.14 across two scenarios—a highway segment with mixed obstacles and a signalized intersection with conflicting turning movements—show that PIPF-TD3 achieves 100% task completion with zero collisions, whereas TD3 without potential field guidance suffers a 90% collision rate. PIPF-TD3 reduces mean cross-track error to 0.12 m (72.1% reduction over the rule-based FSM baseline), maintains 67.0% larger safety clearance, and yields RMS longitudinal and lateral accelerations of 1.12 and 0.75 m/s2, outperforming the FSM by 37.1% and 42.7%. These results confirm that Doppler-weighted physical priors substantially enhance RL-based driving safety and quality in complex traffic conditions.