Human-automated vehicle interactions: Voluntary driver intervention in car-following

Abstract

This paper is concerned with the behavior of voluntary driver interventions in automated vehicles in car-following, initiated by the driver in non-safety-critical situations rather than by the system. Specifically, this study analyzes the dynamic process of voluntary driver intervention through evidence accumulation (EA) modeling, which describes the evolution of the driver’s distrust in automation, ultimately resulting in intervention. The model is calibrated using data from a driving simulator experiment. The experimental data also suggests that driver interventions can instigate substantial traffic disturbances that are amplified through upstream traffic. Based on the findings, we develop a car-following control for AVs by embedding the calibrated EA model in a deep reinforcement learning (DRL) framework. Numerical experiments demonstrate that the proposed control can effectively mitigate unnecessary driver interventions while improving traffic stability. The code supporting the findings of this study are available in Github page.