## 1 Introduction  
In recent years, with the development of artificial intelligence, big data and information processing technology, autonomous vehicles have received more and more attention. Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rates [1; 2; 3]. As the basic part of autonomous vehicle motion control module, path tracking is desired to follow the reference path accurately. It is one of the research hotspots in recent research on autonomous vehicles [4; 5]. This paper mainly focuses on path tracking control for autonomous vehicles. As shown in Figure 1, a path tracking controller was designed based on Ackerman's steering geometry method. Ref. [6] adopted the pure pursuit method, path tracking controllers with deviation-based adaptive preview distance velocity were built and compared with lateral position deviation-based controllers. Ref. [7] proposed a path tracking fuzzy controller based on the clothoid curve-fitting reference path. A preview Stanley path tracking controller was designed in [8]. The vehicle kinematics model was also built to design path tracking controller based on pure pursuit method in [9], shown in Figure 2. This type of method is simple and works well in many situations. However, it is only suitable for the conditions where vehicle speed is low, and the path curvature and curvature change rate are small, due to the lack of consideration of vehicle dynamics. The control accuracy of these methods is obviously dependent on the preview information and the scenario applied. In order to improve the tracking accuracy, [PERSON]. [10; 11; 12] designed controllers using the PID method based on state deviation of vehicle dynamics. The control accuracy and robustness of the PID method performs relatively better and is easily applied. However, the parameters of the PID controller are not always optimal; it requires additional design to self-tune parameters to satisfy control accuracy. Its robustness to time-varying system parameters and external disturbance is not strong due to the control parameters being determined using trial-and-error. The authors of references [13; 14; 15] proposed path tracking controllers in the frame of sliding model control (SMC) based on state feedback information. The radial basis function neural network was used to optimize the sliding mode control output chattering phenomenon in [16; 17]. An enhanced state observer-based SMC strategy was proposed to achieve the control purpose and maintain the lane keeping errors in [18]. The SMC method has strong robustness to model uncertainty and external disturbance. However, the workload and complexity of the path tracking SMC controller are also huge due to eliminating the chattering phenomenon and designing the observer. Under high-speed conditions, the vehicle dynamics information and road curvature information have a great influence on tracking accuracy. The feedforward-feedback control method was used to improve the path tracking accuracy. Ref. [19] considered the influence of rear tires on steering control, and a feedforward-feedback controller was designed to realize the steering yaw stability control under the limited condition of force saturation of rear tires. In [20; 21], the vehicle's steady-state sideslip angle was introduced into the feedback control law. In order to overcome the external disturbance and the uncertainty of model parameters, a feedforward-feedback control method was proposed in [22]. A robust \(H_{\infty}\) output-feedback control strategy was proposed for the path following ability of autonomous ground vehicles in [23]. This type of method requires precise vehicle dynamics information for designing the feedback control law. Therefore, it requires a more accurate sensor to obtain the state information of vehicle dynamics. It is relatively difficult to apply in real time due to the collection and processing of the feedback information.