Adaptive Haptic Assistance Control Considering Individual Driver’s Arm Characteristics

Abstract

To improve the overall performance of human-vehicle cooperation and enhance the drivers’ confidence in the advanced driver assistance system (ADAS), an adaptive haptic assistance control scheme for the steer-by-wire (SBW) vehicle is presented in this paper. A comprehensive human-vehicle system model is built, including vehicle dynamics, the SBW model, and the driver’s arm neuromuscular dynamics model, as a foundation for controller design. An expert driver model based on a multi-layer feed-forward neural network (MLFN) is developed to generate the reference steering angle for haptic assistance design. The individual driver’s arm characteristics are identified and incorporated into the adaptive haptic assistance controller design to generate personalized torque assistance, facilitating a typical driver to achieve the same trajectory-tracking performance as experts. The nonsingular fast terminal sliding mode (NFTSM) is applied to calculate the assistance torque to ensure the fast finite-time convergence and robustness of the system. Simulations and driver-in-the-loop experiments are conducted, with results showing that the proposed haptic assistance controller can help drivers complete the trajectory-tracking task by providing personalized torque assistance while reducing their steering workload.