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

Autor:	Zhong, Xinzhi, Zhou, Yang, Kamaraj, Varshini, Zhou, Zhenhao, Kontar, Wissam, Negrut, Dan, Lee, John D., Ahn, Soyoung
Rok vydání:	2024
Předmět:	Computer Science - Human-Computer Interaction Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.05832 Zobrazit plný text záznamu View this record from Arxiv