Autonomous vehicles require drivers to assume control of the vehicle in situations where the vehicle control system cannot perform its intended task. A shared control-based approach to driving authority transfer can effectively mitigate the driving risks associated with diminished driver capability due to prolonged disengagement, but it may readily precipitate human–machine conflicts—oscillatory steering behavior, excessive driver workload, and unstable control during weight transitions. Addressing the characteristics of driver capability variations during takeover tasks, a shared control strategy incorporating real-time driving ability, termed the real-time driving ability strategy (RDAS), is proposed. Initially, a real-time capability assessment strategy based on an expected steering angle model is developed. By collecting driving data under conditions of adequate driver capability to train an adaptive neuro-fuzzy inference system (ANFIS) neural network, the expected steering angle is predicted, and the deviation between actual and expected steering angles in takeover scenarios of varying difficulty is used to evaluate real-time driver capability. Subsequently, we design a dynamic weight allocation strategy, integrating real-time driving ability and the phased characteristics of driver capability changes during the takeover process. Simulation analysis of driver takeover scenarios demonstrates that, compared to other strategies, even in the case of the smallest performance difference, the RDAS reduces the conflict load (Cl) index by 71.15%, thereby enhancing driving safety and stability in the early and late stages of takeover weight transitions.
Qi et al. (Sat,) studied this question.