  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.
