# Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference
## 1 Introduction  
With the development of new energy and the upgrading of buses, the technology of the driverless bus has become a greater research prospect [1]. Nowadays, many researchers are devoted to path planning and path tracking [2; 3]. One of the most basic and key technologies in driverless buses is path tracking [4]. A path-tracking controller is applied to control the driverless bus to follow the desired path and velocity [5; 6]. Generally, path tracking algorithms are divided into lateral and longitudinal control. The lateral controller controls the driverless bus to track a desired path. Some model-free methods, such as proportional-integral-derivative (PID) [7; 8; 9], fuzzy logic control (FLC) [10; 11], and sliding model control (SMC) [12; 13; 14], are applied. Those algorithms perform well even when the model of the driverless bus is vague. However, they need different parameters to various conditions, which improve the complexity of path-tracking algorithms. [PERSON] proposes an adaptive rolling optimization controller with preview window to track the desired path [15]. It is built on PID, and does not require a high-precision bus model. The robustness of the adaptive rolling optimization controller is poor, too. Meanwhile, some model-based algorithms, such as Stanley, Pure Pursuit (PP) [16], Linear Quadratic Regulator (LQR) [17], and Model Predictive Control (MPC) [18], are provided. The Stanley method is based on the vehicle kinematic model [19]. Stanley uses the lateral error and heading error betweenthe car and the desired path to calculate the steering angle. The tracking accuracy and robustness are good if the velocity is under 15 km/h. However, since the bus's lateral dynamic characteristics are ignored, Stanley performs poorly if the velocity increases to 20 km/h. PP also uses the vehicle kinematic model [16]. Equally, PP performs poorly if the velocity increases. MPC uses the dynamic model of vehicle, and it certainly performs well if the model describes the car well [20]. To improve the stability and tracking accuracy, [PERSON] applies MPC with longitudinal speed compensation [21]. Since the model of the driverless bus is unclear, these model-based methods need to be improved. To solve the problem of path-tracking in driverless buses, a path-tracking controller based on Fuzzy Pure Pursuit Control with a Front Axle Reference (FPPC-FAR) is proposed in this paper. The velocity of the driverless bus is controlled by the longitudinal controller. Usually, PID is applied to track the desired velocity [22]. To reject the unknown disturbance of the car, [PERSON] adopted constant time headway (CTH) strategy to design a longitudinal controller based on MPC [23]. [PERSON] proposed a car path prediction employing yaw acceleration for adaptive cruise control [24]. These studies only consider the model of the car; the influence of road slope is not considered. Since the mass changes a great deal according to the number of passengers in the driverless bus, not only does the model of the driverless bus change significantly, but the road slope also influences the longitudinal control [25]. In this paper, a feedforward controller is introduced to reduce the impact of the road slope. Meanwhile, the PI is used in the feedforward controller to track the desired velocity. The main contributions of this paper are listed as follows:  
1. For lateral control, a FPPC-FAR path-tracking controller is proposed. Firstly, Pure Pursuit Control Based on a Front Axle Reference (PPC-FAR) path-tracking controller is adopted. It moves the reference point of Pure Pursuit to the front axle to relieve the ignorance's influence on lateral control. A coefficient is also added to merge the difference between the proposed model and the real bus. Secondly, the look-ahead distance and coefficient are provided by a fuzzy logic interface machine, which improves the accuracy and robustness of the path-tracking controller. 2. For longitudinal control, a feedback-feedforward control algorithm is constructed.