This paper develops a novel car-following control method to reduce voluntary driver interventions and improve traffic stability in Automated Vehicles (AVs). Through a combination of experimental and empirical analysis, we show how voluntary driver interventions can instigate substantial traffic disturbances that are amplified along the traffic upstream. Motivated by these findings, we present a framework for driver intervention based on evidence accumulation (EA), which describes the evolution of the driver's distrust in automation, ultimately resulting in intervention. Informed through the EA framework, we propose a deep reinforcement learning (DRL)-based car-following control for AVs that is strategically designed to mitigate unnecessary driver intervention and improve traffic stability. Numerical experiments are conducted to demonstrate the effectiveness of the proposed control model.
@article{arxiv.2404.05832,
title = {Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention},
author = {Xinzhi Zhong and Yang Zhou and Varshini Kamaraj and Zhenhao Zhou and Wissam Kontar and Dan Negrut and John D. Lee and Soyoung Ahn},
journal= {arXiv preprint arXiv:2404.05832},
year = {2024}
}