## 1 Introduction  
With the development of intelligent connected technology and vehicle automation technology, intelligent vehicle and multi-vehicle collaborative control have attracted considerable research interests. Vehicle platoon enables multiple vehicles to drive in a formation by automatically adjusting the spacing between the cars. By maintaining a reasonable distance and yaw angle between the adjacent cars, the \"traction effect\" in aerodynamics can effectively save energy and reduce pollutant emissions [1]. Besides, platoon control can increase traffic capacity and reduce the probability of traffic accidents, which was demonstrated in many studies [2; 3; 4]. These potential advantages have led to an increasing emphasis on the research of intelligent connected vehicle collaborative control. With the improvement of platoon automation, considerable researches efforts were devoted to the development of collaborative platoon control in 2-D driving scenes. In 2006, [PERSON] an adaptive cruise control method for path tracking and the side-by-side driving control through the sixth-order dynamics model, and the performances of the front-wheel steering vehicle and the rear-wheel steering vehicle were compared as a following vehicle [5]. In [PERSON]'s study, a robust proportion-integral-derivative (PID) control system was established based on the vehicle dynamics model, which combines path tracking and speed matching to achieve longitudinal and lateral coordinated control [6]. [PERSON] created a three-degree-of-freedom (3-DOF) nonlinear dynamic model, including engine, clutch, automatic transmission, and tire model. The sliding mode control (SMC) controller was designed to test the control effect of the platoon through acceleration and steering driving situations [7]. [PERSON] developed a longitudinal SMC controller to maintain a fixed distance between vehicles and to ensure platoon stability, and the finite state machine was used to realize the conversion of the vehicle controlling state in complicated situations [8]. In [PERSON]'s research, the vehicle's longitudinal and lateral motions were controlled by adaptive fuzzy SMC and it showed good performance [9]. Spacing control of platoon has been intensively investigated, but it remains challenging to develop a path following method for platoon control. Additionally, the above studies are all aimed at traditional front-wheel steering vehicles, while the dynamics characteristics of four-wheel independent driving and steering (FWIDSV) are not considered in platoon control. As a kind of over-actuated vehicle, FWIDSV can control the driving torques and steering angles of front and rear wheels separately, thus can effectively improve the steerability performance and handling stability [10]. As an actuator redundancy system, the primary challenge is dealing with the physical constraints and the actuator redundancy. Most of the solutions are considering it as an optimization problem; therefore, model predictive control theory (MPC) and linear quadratic programming (LQP) are used in tire force allocation [11]. Other studies focus on the pseudo inverse algorithm and design it for dynamically allocating the desired external yaw moment into the redundant tire actuators because of its faster calculation [12]. In 2002, [PERSON] proposed a driver model for self-driving vehicles based on the convergent vector field. By establishing a reference vector field (RVF), non-linear feedback control and residual friction control were used to ensure that the vehicle's driving direction converges to the reference path, and pointed out the application prospect in advanced cruise control systems [13]. Since then, the reference vector field method has been widely studied and applied in the fields of path planning and tracking, obstacle avoidance, and extremum seeking [14; 15; 16].