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The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs)has ushered in an era of mixed traffic flow, presenting a significantchallenge: the intricate interaction between these entities within complexdriving environments. AVs are expected to have human-like driving behavior toseamlessly integrate into human-dominated traffic systems. To address thisissue, we propose a reinforcement learning framework that considers drivingpriors and Social Coordination Awareness (SCA) to optimize the behavior of AVs.The framework integrates a driving prior learning (DPL) model based on avariational autoencoder to infer the driver's driving priors from humandrivers' trajectories. A policy network based on a multi-head attentionmechanism is designed to effectively capture the interactive dependenciesbetween AVs and other traffic participants to improve decision-making quality.The introduction of SCA into the autonomous driving decision-making system, andthe use of Coordination Tendency (CT) to quantify the willingness of AVs tocoordinate the traffic system is explored. Simulation results show that theproposed framework can not only improve the decision-making quality of AVs butalso motivate them to produce social behaviors, with potential benefits for thesafety and traffic efficiency of the entire transportation system.

Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness