## 1 Introduction
### Literature Review  
As the core part of the autonomous lane changing system, path planning is the key to ensuring safe driving. Many scholars have conducted a lot of fruitful research. The following highlights some of the research results. [PERSON] et al. proposed a CC-RRT* Algorithm for autonomous vehicle lane changing in high-speed scenarios [18]. The algorithm based on curve interpolation is the most widely used path planning algorithm for autonomous lane changing of intelligent vehicles. The planned path is usually described in the form of a mathematical function. Different path curves can be obtained by adjusting parameters, such as time and curvature. This method can consider the kinematics and dynamics of the vehicle and can be used for obstacle avoidance in dynamic environments to obtain a safe, stable, and easy-to-track lane changing path curve. The trajectory generated by the lane change trajectory algorithm proposed by [PERSON] et al. based on the curvature of Bezier curve can be used to estimate the maximum lateral acceleration [19]. [PERSON] et al. proposed a method combining a B-spline curve and RRT to plan the lane changing path and monitor the trajectory at the same time to ensure the robustness of the algorithm. However, when the traffic environment is complex, the success rate of generating the lane changing path will decrease [20]. [PERSON] et al. established a second-order continuous quintic spline curve in the Frenet coordinate system, taking into account the curvature and heading angle of the planned path to ensure the safety and stability of the vehicle during the lane change process [21]. In order to obtain an algorithm suitable for autonomous lane change, [PERSON] et al. compared the quintic polynomial function, the sine function, and the tangent function according to the RMS value of peak acceleration and finally chose the quintic polynomial as the lane changing model [22]. Based on the unconstrained lane changing trajectory cluster generated by the quintic polynomial, [PERSON] et al. obtained the feasible lane changing trajectory cluster through vehicle kinematics constraints and comfort requirements and finally determined the optimal lane changing trajectory through the cost function, realizing the safety and stability of the lane changing process [23]. [PERSON] et al. applied deep learning and data-driven methods to polynomial lane changing trajectory optimization so that the final lane changing trajectory followed traffic rules [24]. In recent years, V2V technology has been widely used in autonomous driving. [PERSON] et al. proposed a dynamic autonomous lane changing system based on V2V,which is updated during the lane changing process to avoid possible collision behavior, and uses the lane changing time and distance to transform the planning problem into a constrained optimization problem so that the planned reference trajectories meet safety, comfort, and traffic efficiency requirements [25]. [PERSON] et al. established a path planning model and a speed planning model through cubic polynomial interpolation based on the local trajectory generated by GPS and proposed a comprehensive trajectory optimization function to ensure driving safety, passenger comfort, and lane changing efficiency [2]. [PERSON] et al. proposed a Hermite interpolation algorithm, which can determine the maximum speed of safe driving through the curvature information of the generated path [26]. [PERSON] characterizes lane changing trajectory by combining a linear function and a sine function [27]. [PERSON] et al. obtained the optimal lane changing path, taking into account safety and comfort through a quadratic programming algorithm [28]. [PERSON] et al. combined the bicycle model with the function of automatically determining the target lane and realized the autonomous lane changing of vehicles in the dynamic environment through the model predictive control method (MPC) [29]. [PERSON] et al.