Comparative Study of Path Tracking Controllers on Low Friction Roads for Autonomous Vehicles

Abstract

This paper presents a comparison among path tracking controllers on low-friction roads for autonomous vehicles. There are two goals in this paper. The first is to check the performance of path tracking controllers on low-friction roads, and the second is to check the effectiveness of four-wheel steering (4WS) for path tracking. To fully investigate the performance of path-tracking controllers on low-friction roads in this paper, the pure pursuit method, Stanley method, PID control, linear quadratic regulator, sliding mode control and model predictive control are designed and compared in terms of some measures. Front and four-wheel steering are adopted as actuators for path tracking. To utilize 4WS in the pure pursuit method, Stanley method and PID control, a yaw rate tracking control is adopted. With the designed path tracking controllers, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that most path tracking controllers are effective for path tracking on low-friction roads if finely tuned, and that 4WS is not recommended for path tracking on low-friction roads.

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles

“…PT control approaches in the context of AD address issues related to parametric uncertainties, and external disturbances that cannot be avoided [ 90 ]. Current investigations are performed in the automotive field, where velocities are high, and low-friction roads and roads with irregularities are widespread [ 91 , 92 ].…”

Path Tracking Control with Constraint on Tire Slip Angles under Low-Friction Road Conditions

“…In the context of PTC, a 2-DOF bicycle model has been widely employed to represent yaw and lateral behaviors of a vehicle, as discussed in the literature [25,[30][31][32][33][34][35]39,40]. Figure 2 shows the coordinates, geometry, and variables of the bicycle model.…”

“…The literature on PTC introduced the concepts of lateral offset and heading errors, denoted as e y and e φ , at a specific point C as depicted in Figure 3 . To enhance path tracking performance, this paper introduces a preview function, as used in the previous works [24,26,[30][31][32]. The preview distance, L p , is calculated using Equation ( 5), where k v is the velocity gain representing the preview interval.…”

“…As shown in Table 1, MPC has been widely adopted as a path tracking controller for the last decade because it can handle nonlinear or time-varying systems and nonlinear constraints besides linear time-invariant ones [11,12,14,16,17,[19][20][21][22][27][28][29][30]. Generally, MPC is designed in the discrete-time domain.…”

“…Figure 4 illustrates the tire forces at each wheel and ∆M z when ∆M z is positive [25,[30][31][32]35,39,40]. In Figure 4, four wheels are numbered as 1, 2, 3 and 4 in the order of the front left, front right, rear left and rear right wheels, respectively.…”