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.

翻译：自动驾驶车辆（AV）与人类驾驶车辆（HV）并存开启了混合交通流时代，由此带来重大挑战：复杂驾驶环境中两类实体间的交互行为。为无缝融入人类主导的交通系统，自动驾驶车辆需具备类人驾驶行为。针对该问题，本文提出一种融合驾驶先验与社会协调意识（SCA）的强化学习框架，用于优化自动驾驶车辆行为。该框架基于变分自编码器构建驾驶先验学习（DPL）模型，从人类驾驶轨迹中推断驾驶员的驾驶先验；设计基于多头注意力机制的策略网络，有效捕捉自动驾驶车辆与其他交通参与者之间的交互依赖关系，提升决策质量。探索将社会协调意识引入自动驾驶决策系统，并利用协调倾向（CT）量化自动驾驶车辆协调交通系统的意愿。仿真结果表明，所提框架不仅能提升自动驾驶车辆的决策质量，还能激励其产生社会性行为，从而为整个交通系统的安全性与通行效率带来潜在收益。