# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 1 Introduction  
Autonomous driving is a topic of academic research and industry application that has been widely discussed for almost three decades, and it has now been used on a small scale in the manufacturing of vehicles such as Tesla [1, 2]. A typical autonomous driving system consists of sensing, perception, decision, and control. Sensing and perception refer to sensing the environment around the vehicle using various sensors and understanding it as a means of helping the vehicle make better decisions. Deep learning and artificial intelligence methods are currently widely used in the field of sensing and perception for achieving target detection and recognition through deep learning of a variety of sensor data types. For example, [PERSON] et al. used LiDAR in 3D target detection [3], [PERSON] et al. proposed UIU-Net for infrared object detection [4] using remote sensing images for the detection of weak targets [5, 6], and the well-known YOLO [7] uses RGB cameras for pedestrian detection. At the same time, the purpose of decision-making and control is to ensure that the vehicle travels on an appropriate path at an appropriate speed based on the sensing results. The focus of this paper is on path tracking, which is part of the control module. The objective of this is the manipulation of the steering wheel and throttle pedal to guide the vehicle to follow the desired path [8]. The optimization control methods that are used in path tracking algorithms can be classified into five categories [9]: (1) Pure pursuit and the Stanley control method. [PERSON] et al. [10] proposed a novel pure pursuit algorithm based on optimized look-ahead distance as a means of improving tracking accuracy and [PERSON] et al. [11] proposed a model predictive active yaw control implementation of pure pursuit path tracking for improving tracking performance at high speed. These controllers are based on the geometric concept, using the kinematics model and geometric relation to calculate target steering angles. They are simple and suitable in many low-vehicle-speed applications but may experience problems if the vehicle is involved in a situation with large path curvature. (2) The PID control method: PID control-based algorithms are widely used in autonomous driving. [PERSON] et al. [12] established a neural network PID controller for lateral path tracking, while [PERSON] et al. [13] utilized a fractional order PID controller for achieving autonomous path tracking. This method can easily be used in engineering applications, but there is the disadvantage that the algorithms and parameters are application-dependent. (3) Model-free method: This approach is essentially data-driven, such as that proposed by [PERSON] et al. [14] where a novel model-free adaptive control algorithm based on a dual successive projection method used at least five sensor types to generate sufficient data for the algorithm to learn the control parameters, which may present difficulties in situations with insufficient sensor data. (4) The Linear Quadratic Regulator (LQR) method: The LQR controller is a classic method that is used in path tracking. For example, in [15], LQR is adopted as a means of obtaining the optimal active front steering input. LQR controllers require high computing power due to being optimized online. (5) The Model Predictive Control (MPC) method: This [16] has the ability to predict future road curvature and act accordingly. Many researchers have proposed different enhanced MPC controllers for improving performance. [PERSON] et al. [17] combined MPC with particle swarm optimization (PSO) in order to achieve path tracking using four-wheel steering and four-wheel drive (4 WS4 WD). [PERSON] et al. [18] used a kinematic MPC for dealing with disturbances in road curvature and a vehicle sideslip angle compensator for correcting kinematic model prediction.