## 1 Introduction
### Related Work  
As an important part of the CLC maneuver, the CLC control strategies have been studied to control the CAVs for performing safe and smooth lane changes. For the trajectory planning-tracking control strategy, [PERSON] et al. [31] proposed a trajectory planning and tracking scheme for multi-CAVs CLC under multi-lane scenarios. This trajectory planning mode is based on multi-objective optimization problems, and the trajectory tracking mode is based on an MPC controller. [PERSON] et al. [32] put forward a cooperative control framework for connected HDVs to cooperate with the CAV to help them carry out safe and efficient lane-changing maneuvers. This research is based on MPC control theory to solve the multi-vehicle interaction and multi-constraint motion-planning problem. For heterogeneous platoons scenarios, [PERSON] et al. [33] proposed a CLC approach for heterogeneous platoons under different communication topologies when the platoon encounters a slow obstacle vehicle ahead. This study first performs trajectory planning for the leading vehicle, and then the platoon vehicles behind the leading vehicle perform simultaneous lane changes using a distributed model predictive control (MPC) algorithm. [PERSON] et al. [34] explored the possibility of accelerating the cooperation of leading vehicles on the target lane and proposed a dynamic CLC control model. Different trajectory planning methods are used in this model for lane change vehicles and cooperative vehicles, but the same trajectory-tracking algorithm was used for all controlled CAVs. For the integrated single-step optimization control strategy, by considering the concurrent mandatory lane change requirements, [PERSON] et al. [29] proposed a CLC trajectory planning method for multiple CAVs by using a nonlinear programming approach with multiple safety and efficiency constraints. For diverging the highway off-ramp, [PERSON] et al. [35] put forward a cooperative lane-changing strategy to improve traffic operation and safety at a diverging area nearby a highway off-ramp. Based on the cooperative control strategy constructed by the modified Minimizing Overall Braking Induced by the Lane Changes Model (MOBIL) and modified Intelligent Driver Model (IDM) to control the coordination of behaviors between the diverging vehicle and the cooperative vehicle on the target lane. As a typical representative of an integrated rolling-horizon optimization control strategy, MPC has been applied to the field of heterogeneous vehicle platoon control due to its convenient model building and great dynamic control performance [36]. Due to the excellent control effect of MPC in the field of longitudinal platoon control, some scholars have tried to utilize MPC to solve the lateral CLC control problem. To clarify the control process of lane change, [PERSON] et al. [30] divided the CLC control into two steps, namely feasibility determination and distributed control. An incentive model is used to evaluate the lane change feasibility in terms of acceleration capability and passenger comfort. An MPC-based approach was proposed to collaborate the movement of CAVs. This study [30] divides the complex cooperative lane-changing control process into the lane change phase and the longitudinal headway adjustment phase to predigest the problem-solving of the simplified MPC. [PERSON] et al. [37] presented a CLC motion planning algorithm for partially connected and automated environments. This algorithm reduces the oscillations and shockwaves caused by the lane-changing process. To summarize, CLC control strategies mainly include trajectory planning-tracking CLC control strategy and integrated CLC control strategy. The integrated CLC control strategies have many advantages over the trajectory planning-tracking CLC control strategies, such as the simplicity of single optimization algorithm structure and dynamic adaptability, so this type of control strategy is used in this study.