Evaluation of drivers' interaction ability at social scenarios: a process-based framework

Abstract

Assessing drivers' interaction capabilities is crucial for understanding human driving behaviour and enhancing the interactive abilities of autonomous vehicles. In scenarios involving strong interaction, existing metrics focussed on interaction outcomes struggle to capture the evolutionary process of drivers' interactive behaviours, 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 behaviour 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.

Keywords:

• Interaction ability evaluation
• game theory
• driving risk perception
• trajectory similarity measures

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China (52232015), in part by the Fundamental Research Funds for the Central Universities (No. 2022-5-ZD-02 and No. 22120220434), in part by the Zhejiang Laboratory (2021NL0AB02), and in part by Postdoctoral Fellowship Program of CPSF(GZC20231893).