Interacting with other human road users is one of the most challenging tasks for autonomous vehicles. To generate congruent driving behaviors, the awareness and understanding of sociality, which includes implicit social customs and individualized social preferences of human drivers, are required. To understand and quantify the complex sociality in driving interactions, we propose a Virtual-Game-based Interaction Model (VGIM) that is explicitly parameterized by a social preference measurement, Interaction Preference Value (IPV), which is designed to capture the driver's relative preference for individual rewards over group rewards. A method for identifying IPV from observed driving trajectory is also provided. Then, we analyze human drivers' IPV with driving data recorded in a typical interactive driving scenario, the unprotected left turn. The results show that (1) human drivers express varied social preferences in executing different tasks (turning left or going straight); (2) competitive actions are strategically conducted by human drivers in order to coordinate with others. Finally, we implement the humanlike IPV expressing strategy with a rule-based method and embed it into VGIM and optimization-based motion planners. Controlled simulation experiments are conducted, and the results demonstrate that (1) IPV identification could improve the motion prediction performance in interactive driving scenarios and (2) dynamic IPV expressing strategy extracted from human driving data makes it possible to reproduce humanlike coordination patterns in the driving interaction.

翻译：与其他人类道路使用者的交互是自动驾驶汽车面临的最具挑战性的任务之一。要生成协调一致的驾驶行为，需要理解并感知社会性——这包括人类驾驶员隐含的社会习俗和个性化的社会偏好。为了理解和量化驾驶交互中复杂的社会性，我们提出了一种基于虚拟博弈的交互模型（VGIM），该模型通过社会偏好度量——交互偏好值（IPV）进行显式参数化，旨在捕捉驾驶员对个体奖励相对于群体奖励的偏好程度。同时，本文还提出了一种从观测驾驶轨迹中识别IPV的方法。随后，我们利用典型驾驶交互场景（无保护左转）中记录的驾驶数据分析了人类驾驶员的IPV。结果表明：（1）人类驾驶员在执行不同任务（左转或直行）时表现出不同的社会偏好；（2）人类驾驶员为协调他人会策略性地采取竞争性行为。最后，我们采用基于规则的方法实现了类人IPV表达策略，并将其嵌入VGIM和基于优化的运动规划器中。通过受控仿真实验，结果表明：（1）IPV识别能提升交互驾驶场景下的运动预测性能；（2）从人类驾驶数据中提取的动态IPV表达策略可再现驾驶交互中的类人协调模式。