Path-tracking control strategy for enhanced comfort in all-wheel-steering autonomous vehicles

In this paper, a path-tracking controller is developed for an autonomous vehicle with All-Wheel-Steering (AWS) capability. Based on nonlinear model predictive control, the proposed controller is formulated in a way that allows the manipulation of vehicle’s attitude during path-tracking. With high-fidelity vehicle dynamics simulation, the controller is examined at various velocities up to the limit handling condition. Comparison is carried out in the aspects of path-tracking, ride comfort and motion sickness, between the implementation with a constant yaw angle reference (referred to as crab steering) and the nominal steering behaviour for negotiating the same path. The ride comfort metric suggested by ISO-2631 is used to capture the benefits of the crab steering approach against the nominal case, and the simulation results reveal that crab steering is able to enhance the ride comfort for AWS vehicles in double lane-change and slalom manoeuvres.