  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.
