# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 1 Introduction  
The casualties and economic losses caused by road traffic accidents are increasing year by year. In the face of such serious traffic safety problems, it is urgent to improve automobile safety performance. Intelligent vehicles, with their capacity to reduce traffic accidents, enhance transport efficiency, and offer promising market prospects, have garnered significant attention. They hold the potential to drive forward the future of the automotive industry [1]. Self-driving technology is the key technology of vehicle active safety, which can effectively reduce the loss of personnel and property caused by traffic accidents. A good control algorithm is the premise of obstacle avoidance and trajectory tracking in a driverless vehicle. Researchers, both domestically and internationally, have extensively investigated this topic. To address the problem of path planning and trajectory tracking, [PERSON] et al. [2] used adaptive dynamic planning (ADP) and the APF method to solve the dynamic path planning problem. It has been proven that this method can plan the obstacle avoidance path in both dynamic and static environments. To improve the safety of the planned obstacle avoidance paths, [PERSON] et al. [3] proposed a novel path-planning approach utilizing enhanced genetic algorithms and dynamic window methods. [PERSON] et al. [4] proposed an adaptive model predictive controller through fuzzy adaptive control methodsto achieve the adaptive optimization of the weights of the objective function in the model predictive, aiming to enhance control effectiveness. The trajectory tracking method based on optimal preview was mainly proposed by [PERSON] [5; 6] and improved gradually. In this method, the vehicle is considered to be in uniform motion or a variable speed motion with slow speed change, so that the vehicle's horizontal and vertical controls are decoupled. Path tracking is accomplished using lateral control, and speed tracking is accomplished using longitudinal control. In order to better imitate the method of a real driver's driving, [PERSON] et al. [7] considered the multi-point preview inaccuracy of the path, designed an MPC controller using a dynamic model, and constructed an articulated vehicle model using dynamic model predictive control, which improved the stability under limited operating conditions. [PERSON] et al. [8] proposed a steering strategy based on fuzzy logic to realize parameter self-adjustment based on displacement error to make its trajectory tracking more stable and accurate. In order to improve the parameter uncertainty of the bicycle model, [PERSON] et al. [9] designed a longitudinal controller based on adaptive estimation of dynamic surfaces through a neural network. The experimental data show that the controller drives safely within the operation limit of the vehicle. [PERSON] et al. [10] investigated the utilization of fuzzy controllers and model predictive controllers in intelligent vehicle trajectory tracking control. And on this basis, the PSO algorithm was used to study the parameter optimization of the prediction time domain and control time domain in the model predictive controller. [PERSON] et al. [11] optimized the PID longitudinal speed controller. The controller has better real-time performance and tracking accuracy than the PSO algorithm-based controller. [PERSON] et al. [12] designed a reverse-tracking control based on disturbance observer, which is used to estimate and track the reference signal of electro-hydraulic actuator (EHA) system in finite time. The key idea is to use the monotone increasing function related to the control target to improve the control performance, in which Lyapunov stability analysis is used to guarantee the finite time boundedness criterion. The proposed method has higher accuracy and faster convergence. [PERSON] et al.