Driving with Haptic Shared Control (HSC) provides an alternative for traditional traded control in human-controller interaction. Whilst driving, control is shared between the driver and the controller by translating the controller's desired steering input to additional torques on the steering wheel.
Literature provides several guidelines for the tuning of these torques but not for the tuning of the interaction stiffness around the controller's desired steering input: the Level of Haptic Authority (LoHA), this is usually kept constant.
High LoHA tunings have beneficial effects on the driver performance but result in high conflict torques and negatively impact the driver acceptance.
In this study two adaptive LoHA algorithms are proposed based on Time to Lane Crossing (TLC). By increasing the LoHA in critical, low-TLC, scenarios these should improve performance while also allowing for a larger steering freedom and low conflicts in safe scenarios.
The adaptive LoHA is applied symmetrically (bi-directional) and asymmetrically (only in the direction of the low TLC).
The adaptive algorithms are compared to manual driving, a low and a high static LoHA tuning in a within-subject driving simulator study. Fourteen participants performed a lane keeping task in which lane width varied to influence the safety margins and TLC.
While driving with adaptive LoHA, the mean conflict torque was significantly lower for the adaptive algorithms than for the high stiffness controller and participants experienced lower workloads. However, no difference was found between the symmetric and asymmetric LoHA controller. These results show that adaptive LoHA based on TLC is an effective way to achieve a similar performance as with static LoHA but with lower conflicts and a lower workload.