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.

翻译：本文提出了一种新颖的跟驰控制方法，旨在减少自动驾驶汽车（AVs）中驾驶员的自愿干预行为，并提升交通稳定性。通过实验与实证相结合的分析，我们揭示了驾驶员自愿干预如何引发显著的交通扰动，且该扰动会在上游交通流中不断放大。基于这些发现，我们提出了基于证据积累（EA）的驾驶员干预框架，该框架描述了驾驶员对自动化系统信任度降低直至最终实施干预的演变过程。在EA框架的指导下，我们设计了一种基于深度强化学习（DRL）的AV跟驰控制策略，旨在战略性地减少不必要的驾驶员干预并提升交通稳定性。数值实验验证了所提控制模型的有效性。