A workload adaptive haptic shared control scheme for semi-autonomous driving

• Published in: Accident analysis and prevention 2021-03, Vol.152, p.105968-105968, Article 105968
• Main Authors: Luo, Ruikun, Weng, Yifan, Wang, Yifan, Jayakumar, Paramsothy, Brudnak, Mark J., Paul, Victor, Desaraju, Vishnu R., Stein, Jeffrey L., Ersal, Tulga, Yang, X. Jessie
• Format: Article
• Language: English
• ISSN: 0001-4575; 1879-2057
• DOI: 10.1016/j.aap.2020.105968

•We develop a shared control scheme that adapts to the human operator's workload in real time.•We propose a heuristic design of the adaptive scheme based on human factors theories.•We estimate the human operators’ workload by using HMM to analyze their gaze trajectory.•The haptic control scheme results in significantly lower workload, higher trust in autonomy, better driving task performance and smaller control effort. Haptic shared control is used to manage the control authority allocation between a human and an autonomous agent in semi-autonomous driving. Existing haptic shared control schemes, however, do not take full consideration of the human agent. To fill this research gap, this study presents a haptic shared control scheme that adapts to a human operator's workload, eyes on road and input torque in real time. We conducted human-in-the-loop experiments with 24 participants. In the experiment, a human operator and an autonomy module for navigation shared the control of a simulated notional High Mobility Multipurpose Wheeled Vehicle (HMMWV) at a fixed speed. At the same time, the human operator performed a target detection task. The autonomy could be either adaptive or non-adaptive to the above-mentioned human factors. Results indicate that the adaptive haptic control scheme resulted in significantly lower workload, higher trust in autonomy, better driving task performance and smaller control effort.