With the commercial application of automated vehicles (AVs), the sharing of roads between AVs and human-driven vehicles (HVs) becomes a common occurrence in the future. While research has focused on improving the safety and reliability of autonomous driving, it's also crucial to consider collaboration between AVs and HVs. Human-like interaction is a required capability for AVs, especially at common unsignalized intersections, as human drivers of HVs expect to maintain their driving habits for inter-vehicle interactions. This paper uses the social value orientation (SVO) in the decision-making of vehicles to describe the social interaction among multiple vehicles. Specifically, we define the quantitative calculation of the conflict-involved SVO at unsignalized intersections to enhance decision-making based on the reinforcement learning method. We use naturalistic driving scenarios with highly interactive motions for performance evaluation of the proposed method. Experimental results show that SVO is more effective in characterizing inter-vehicle interactions than conventional motion state parameters like velocity, and the proposed method can accurately reproduce naturalistic driving trajectories compared to behavior cloning.

翻译：随着自动驾驶汽车（AVs）的商业化应用，未来道路上将普遍出现AVs与人类驾驶车辆（HVs）共享路权的场景。现有研究主要聚焦于提升自动驾驶的安全性与可靠性，但AVs与HVs的协作能力同样至关重要。类人交互是AVs应具备的关键能力，尤其是在常见的无信号灯路口场景中，HV的人类驾驶员期望在车辆交互过程中保持其固有驾驶习惯。本文在车辆决策过程中引入社会价值取向（SVO）来描述多车辆间的社会交互行为。具体而言，我们定义了无信号灯路口冲突场景下SVO的量化计算方法，以强化学习方法为基础优化决策过程。采用包含高动态交互行为的自然驾驶场景对所提方法进行性能评估。实验结果表明，相较于速度等传统运动状态参数，SVO能更有效地表征车辆间交互特征；与行为克隆方法相比，所提方法能够精确复现自然驾驶轨迹。