Planning and tracking control of full drive-by-wire electric vehicles in unstructured scenario

Abstract

Full drive-by-wire electric vehicles (FDWEV) equipped with X-by-wire technology can enable independent driving, braking, and steering of each wheel, making them an ideal platform for developing autonomous driving technology. However, designing a robust control algorithm that comprehensively integrates vehicle path planning in a complex and unstructured scenario is a challenging task for FDWEV. To address this issue, this paper (1) proposes the artificial potential field (APF) method for path planning in the prescribed park with different static obstacles to generate the reference path information, where speed planning is incorporated considering kinematics and dynamic constraints; (2) designs curvature calculation (CC-based) and model predictive control (MPC-based) tracking methods with the lateral dynamics model to track the desired path under different driving conditions, in which a forward-looking behavior model of the driver with variable preview distance is designed based on the fuzzy control theory; (3) conducts CarSim-AMESim-Simulink co-simulation with the existence of obstacles. The simulation results show that the proposed two control approaches are practical for classical driving scenarios. Especially the MPC-based path-tracking controller enhances dynamic tracking performance and ensures good maneuverability under high-dynamic driving conditions.