## 1 Introduction  
With the development of autonomous driving technology, autonomous vehicles have shown great value in improving vehicle safety, enhancing traffic efficiency, liberating the driver's hands, etc., which has attracted more and more attention from relevant scholars [1]. One of the most challenging and significant research issues for autonomous vehicle is the smooth and accurate automatic path tracking control, which is important for insuring the driving safety and accurately tracking the reference trajectory [2]. The main goal of the automatic path tracking control system is to guarantee the lateral path following errors with maintaining the lateral driving stability and considering the dynamic constraints of autonomous vehicle. Many vehicle control algorithms have been presented to obtain active safety and enhance the lateral dynamic stability and path tracking performance. In [3], the authors presented a robust control algorithm of path tracking with considering the problem of delay and data dropout for autonomous vehicle. In [4], the active disturbance rejection control method and differential flatness theory were used for lateral path tracking control of autonomous vehicle. In [5] the disturbances within a finite preview window were augmented as the state vector and a preview steering control strategy was presented for linear augmented error system. However, all these automatic control strategies focus on constant velocity. Due to the inevitable variation of vehicle longitudinal velocity, the previous studies on a fixed constant longitudinal velocity face a significant limitation in real driving conditions. To track the reference path within limited velocity without collisions, the autonomous vehicle needs to accurately acquire the motion states information and percept the immediate physical traffic environment. Therefore, numerous sensors are adopted for autonomous vehicles, such as camera, inertial measurement unit (IMU), LIDAR, RADAR and GPS (Global Positioning System) integrated navigation system. The effective working conditions of each type of sensors are not the same. For example, the camera cannot work normally when the illumination conditions are insufficient. The detection accuracies of RADAR and LIDAR sensors are easily affected by bad weather such as smoke, rain, and snow. The GPS signal is easily blocked in the environment with tall trees, urban buildings, and tunnels, which can cause signal interruption. Therefore, the sensors of autonomous vehicle may be influenced by illumination, signal outage, poor weather conditions, magnetic interference, and other environmental circumstances. In addition, the sensors age and inherent errors may also result in sensor failure and affecting data availability. All these issues may be the reasons prompting the transmission of faulty information [6; 7; 8]. The possibility of sensors fault increases with the numbers and types of vehicle sensors, which can result in disruptive consequences and fatal crashes. The method of sensor redundancy and data fusion can be used to avoid sensor fault. The same motion parameters of an autonomous vehicle can be obtained by multiple different sensors [9]. For instance, the vision system used for vehicle detection can be affected by traffic lights and high-brightness lights of vehicle in nighttime; however, RADAR and LIDAR would not be affected in these cases. Hence, by using the method of sensor redundancy, the impact of sensor fault can be reduced into an acceptable level. However, the reliability of measured parameters after data fusion is still doubtful, if the faulty sensor data are not detected and isolated. Many methods for sensor fault detection have been proposed; however, few of them are focused on autonomous vehicles. Some methods use the distances as the metrics for sensor fault detection, for instance the Mahalabobis distance, affinity propagation clustering, and graph theory [10; 11; 12; 13]. In [14; 15], the Kalman filtering method was used for fault detection.