Interacting with other human road users is one of the most challenging tasks for autonomous vehicles. For congruent driving behaviors, it is essential to recognize and comprehend sociality, encompassing both implicit social norms and individualized social preferences of human drivers. To understand and quantify the complex sociality in driving interactions, we propose a Virtual-Game-based Interaction Model (VGIM) that is parameterized by a social preference measurement, Interaction Preference Value (IPV). The IPV is designed to capture the driver's relative inclination towards individual rewards over group rewards. A method for identifying IPV from observed driving trajectory is also developed, with which we assessed human drivers' IPV using driving data recorded in a typical interactive driving scenario, the unprotected left turn. Our findings reveal that (1) human drivers exhibit particular social preference patterns while undertaking specific tasks, such as turning left or proceeding straight; (2) competitive actions could be strategically conducted by human drivers in order to coordinate with others. Finally, we discuss the potential of learning sociality-aware navigation from human demonstrations by incorporating a rule-based humanlike IPV expressing strategy into VGIM and optimization-based motion planners. Simulation experiments demonstrate that (1) IPV identification improves the motion prediction performance in interactive driving scenarios and (2) the 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进行交互是自动驾驶汽车面临的最大挑战之一。为实现协调一致的驾驶行为，必须识别并理解社会性，这既包括隐性的社会规范，也包括人类驾驶员的个性化社会偏好。为了量化和理解驾驶交互中的复杂社会性，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参数——交互偏好值（IPV）进行参数化。IPV旨在捕捉驾驶员在群体奖励与个体奖励之间的相对倾向。我们进一步开发了从观测驾驶轨迹中识别IPV的方法，并利用典型交互驾驶场景（无保护左转）中记录的驾驶数据评估了人类驾驶员的IPV。研究结果表明：（1）人类驾驶员在执行特定任务（如左转或直行）时会表现出特定的社会偏好模式；（2）人类驾驶员可能采取策略性竞争行为以实现与他人的协调。最后，我们探讨了通过将基于规则的类人IPV表达策略融入VGIM与优化型运动规划器，从人类示范中学习社会感知导航的潜力。仿真实验证明：（1）IPV识别可提升交互驾驶场景中的运动预测性能；（2）从人类驾驶数据中提取的动态IPV表达策略能够再现驾驶交互中的类人协调模式。