  The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs)
has ushered in an era of mixed traffic flow, presenting a significant
challenge: the intricate interaction between these entities within complex
driving environments. AVs are expected to have human-like driving behavior to
seamlessly integrate into human-dominated traffic systems. To address this
issue, we propose a reinforcement learning framework that considers driving
priors and Social Coordination Awareness (SCA) to optimize the behavior of AVs.
The framework integrates a driving prior learning (DPL) model based on a
variational autoencoder to infer the driver's driving priors from human
drivers' trajectories. A policy network based on a multi-head attention
mechanism is designed to effectively capture the interactive dependencies
between AVs and other traffic participants to improve decision-making quality.
The introduction of SCA into the autonomous driving decision-making system, and
the use of Coordination Tendency (CT) to quantify the willingness of AVs to
coordinate the traffic system is explored. Simulation results show that the
proposed framework can not only improve the decision-making quality of AVs but
also motivate them to produce social behaviors, with potential benefits for the
safety and traffic efficiency of the entire transportation system.
