SAFE-AD: Socially aware field-enhanced reinforcement learning for autonomous driving in interactive traffic

Abstract An autonomous vehicle can complete its route while disrupting surrounding traffic. We introduce SAFE-AD, augmenting reinforcement learning (RL) observations with compact descriptors of a finite-horizon interaction-pressure field constructed from current traffic states. The complete objective supplements the environment's task reward with risk, comfort, and externality costs. Then, we formulate a context-conditioned physics-informed neural network (PINN) to approximate the numerical field. Evaluation combines held-out naturalistic recordings, HighwayEnv diagnostics, and closed-loop MetaDrive tests in various traffic scenarios, with a matched factorial experiment with continuous soft actor-critic (SAC) separates observation and objective effects. At unprotected intersections, the complete profile reduces collision rate from 0.296 to 0.220 and mean absolute jerk by 0.98 m/s3 relative to baseline SAC. Besides, the mean absolute jerk in roundabout decreases by 1.54 m/s3. The results show scenario-specific gains and sensitivity to objective calibration.