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.

This paper supplies a roadmap on how a researcher can effectively perform real vehicular experiments oriented to high-speed driving research. It provides detailed guidelines for constructing versatile low-cost instrumentation suitable to be fitted on race cars. The custom-built equipment, consisting of wheel-speed sensors, steering angle-torque sensors, electronic boards, etc., is thoroughly described. Furthermore, this paper depicts the required processing from raw measurements to user-friendly data suitable for driver behavior studies. As an illustration, a case study on driving behavior analysis is presented, during the execution of high-speed circular maneuvers. The recorded data showed markedly different driving behaviors between expert and novice drivers. The mechanical designs and the open-source-based software are freely available online.