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.
