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arXiv · 2404.05832

Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention

Xinzhi Zhong·Yang Zhou·Varshini Kamaraj·Zhenhao Zhou·Wissam Kontar·Dan Negrut·John D. Lee·Soyoung Ahn
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Abstract

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.

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Xinzhi Zhong, Yang Zhou, Varshini Kamaraj, Zhenhao Zhou, Wissam Kontar, Dan Negrut, John D. Lee, Soyoung Ahn. 2024-04-08. Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention. https://arxiv.org/abs/2404.05832

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