Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910402702475264 |
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| author | Zhong, Xinzhi Zhou, Yang Kamaraj, Varshini Zhou, Zhenhao Kontar, Wissam Negrut, Dan Lee, John D. Ahn, Soyoung |
| author_facet | Zhong, Xinzhi Zhou, Yang Kamaraj, Varshini Zhou, Zhenhao Kontar, Wissam Negrut, Dan Lee, John D. Ahn, Soyoung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05832 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention Zhong, Xinzhi Zhou, Yang Kamaraj, Varshini Zhou, Zhenhao Kontar, Wissam Negrut, Dan Lee, John D. Ahn, Soyoung Human-Computer Interaction Systems and Control 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. |
| title | Human-Machine Interaction in Automated Vehicles: Reducing Voluntary Driver Intervention |
| topic | Human-Computer Interaction Systems and Control |
| url | https://arxiv.org/abs/2404.05832 |