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

Authors:
  • ZWZian Wang
    Zian Wang
  • WHWenjie Huang
    Wenjie Huang
  • ZDZejian Deng
    Zejian Deng
  • YSYiming Shu
    Yiming Shu
  • JXJiahui Xu
    Jiahui Xu
  • YWYong Wang
    Yong Wang
  • SLShen Li
    Shen Li
  • DCDongpu Cao
    Dongpu Cao
Open Access
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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.