  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 for surveillance. 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.
