Assessing drivers' interaction capabilities is crucial for understanding human driving behavior and enhancing the interactive abilities of autonomous vehicles. In scenarios involving strong interaction, existing metrics focused on interaction outcomes struggle to capture the evolutionary process of drivers' interactive behaviors, making it challenging for autonomous vehicles to dynamically assess and respond to other agents during interactions. To address this issue, we propose a framework for assessing drivers' interaction capabilities, oriented towards the interactive process itself, which includes three components: Interaction Risk Perception, Interaction Process Modeling, and Interaction Ability Scoring. We quantify interaction risks through motion state estimation and risk field theory, followed by introducing a dynamic action assessment benchmark based on a game-theoretical rational agent model, and designing a capability scoring metric based on morphological similarity distance. By calculating real-time differences between a driver's actions and the assessment benchmark, the driver's interaction capabilities are scored dynamically. We validated our framework at unsignalized intersections as a typical scenario. Validation analysis on driver behavior datasets from China and the USA shows that our framework effectively distinguishes and evaluates conservative and aggressive driving states during interactions, demonstrating good adaptability and effectiveness in various regional settings.

翻译：评估驾驶员的交互能力对于理解人类驾驶行为及提升自动驾驶车辆的交互能力至关重要。在涉及强交互的场景中，现有聚焦于交互结果的评估指标难以捕捉驾驶员交互行为的演化过程，导致自动驾驶车辆在交互过程中难以动态评估并响应其他智能体。为解决此问题，我们提出了一种面向交互过程本身的驾驶员交互能力评估框架，该框架包含三个组成部分：交互风险感知、交互过程建模与交互能力评分。通过运动状态估计与风险场理论量化交互风险，进而引入基于博弈论理性智能体模型的动态动作评估基准，并设计了基于形态相似性距离的能力评分指标。通过实时计算驾驶员动作与评估基准之间的差异，动态评估驾驶员的交互能力。我们以无信号交叉口作为典型场景对该框架进行了验证。基于中美两国驾驶员行为数据集的验证分析表明，该框架能够有效区分并评估交互过程中的保守与激进驾驶状态，在不同区域场景下展现出良好的适应性与有效性。