Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention
<|reference_start|>Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention: 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.<|reference_end|>
@article{zhong2024human-machine,
    title={Human-Machine Interaction in Automated Vehicles: Reducing Voluntary
  Driver Intervention},
    author={Xinzhi Zhong, Yang Zhou, Varshini Kamaraj, Zhenhao Zhou, Wissam
  Kontar, Dan Negrut, John D. Lee, Soyoung Ahn},
    journal={arXiv preprint arXiv:2404.05832},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05832},
    primaryClass={cs.HC cs.SY eess.SY}
}