Path-following control is one of the key technologies of autonomous vehicles, but the complex coupling effects and system uncertainties of vehicles can degrade their control performance. Accordingly, this study proposes targeted methods to solve different types of coupling in vehicle dynamics. First, the types of coupling are figured out and different handling strategies are proposed for each type, among which the coupling caused by steering angle, unsaturated tire forces, and load transfer can be treated as uncertainties in a unified form, such that the coupling effects can be treated in a decoupling way. Then, robust control methods for both lateral and longitudinal dynamics are proposed to deal with the uncertainties in dynamic and physical parameters. In lateral control, a robust feedback–feedforward scheme is utilized in lateral control to deal with such uncertainties. In longitudinal control, a radial basis function neural network-based adaptive sliding mode controller is introduced to deal with uncertainties and disturbances. In addition, the tire saturation coupling that cannot be handled by controllers is treated by a proposed speed profile. Simulation results based on the CarSim–Simulink joint platform evaluate the effectiveness and robustness of the proposed control method. The results show that compared with a well-designed robust controller, the velocity tracking performance, lateral tracking performance, and heading tracking performance improve by 55.68%, 34.26%, and 52.41%, respectively, in the double-lane change maneuver, and increase by 87.79%, 30.18%, and 9.68%, respectively, in the ramp maneuver.