@article{Wang2026, 
author = {Zian Wang and Wenjie Huang and Zejian Deng and Yiming Shu and Jiahui Xu and Yong Wang and Shen Li and Dongpu Cao and Chen Sun},
title = {SAFE-AD: Socially aware field-enhanced reinforcement learning for autonomous driving in interactive traffic},
year = {2026},
journal = {Communications in Transportation Research},
keywords = {Autonomous driving, socially aware reinforcement learning, physics-informed neural networks, spatiotemporal risk field, tactical behavior planning, externality-aware decision making.},
url = {https://www.sciopen.com/article/10.26599/COMMTR.2026.9640067},
doi = {10.26599/COMMTR.2026.9640067},
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.  }
}