## 1 Introduction
### Motivations
Integrated lane change control strategy can be divided into two methods: single-step optimization [28; 29] and rolling-horizon optimization control strategies [27; 30]. The single-step optimization control strategy is usually given the CLC initial and terminal traffic states. However, the given terminal state of the lane change may make the model less adaptable to the dynamic environment. The rolling-horizon optimization control strategy can solve the optimization iteratively in a given time domain and move forward in time in each solution step, which can improve the adaptability to a dynamic traffic environment. Even though various CLC control strategies have been studied, some conventional assumptions may impede the further improvement of lane change control strategies in terms of traffic flow efficiency. We discuss three major unrealistic assumptions which motivate this research in the following: (i) most CLC control strategies with independent analysis of lane change mainly focus on the modeling of the longitudinal motions of the controlled CAVs during the lane-changing process and the interaction between the longitudinal and lateral movements of the lane change is not considered [27; 30]. However, completely ignoring the influence between the lateral and longitudinal motions may reduce the environmental adaptability of the model. (ii) The preceding vehicle switching process in lane change is oversimplified in those studies. They usually assume no preceding vehicle on the original lane or instantaneously preceding vehicle switch for all controlled CAVs [27; 28; 30]. However, the preceding vehicles of the lane-changing vehicle and the following vehicle in the target lane may switch during the lane-changing process. Ignoring the preceding vehicle switching process may lead to a disturbance in the longitudinal controller for the following vehicles in the target lane and may even cause traffic oscillations and traffic accidents in the target lane. (iii) To simplify the problem-solving, most studies usually divide the whole CLC control into two independent stages: lane change control and longitudinal tracking control [27; 28; 30]. Infact, after the lane-changing vehicle enters the target lane, it can perform longitudinal tracking adjustment while continuing to execute the lane change lateral motion, so this division is somewhat unreasonable. Motivated by the aforementioned overlooked assumptions, this paper proposes an MPC-based CLC control strategy for a two-lane highway under a pure CAV traffic flow. In this work, the lane change control is divided into different stages according to the lane in which the lane change vehicle is laterally positioned during the lane change. Based on the architecture of lateral and longitudinal separation of the lane change, the lateral motion of a lane change is described by an appropriate trajectory equation and it is integrated into the process of the longitudinal motion model building. For the longitudinal motion of the lane-changing process, a CLC optimization control strategy is established based on MPC for obtaining the optimal control input of each CAV in real-time. This strategy achieves a safe and effective lane change while considering the correlation between the lateral and longitudinal movements of lane change and preceding vehicle switching of each controlled CAV. In order to verify the performance of the proposed method, two typical lane-changing scenarios are selected to perform a comparative analysis between the proposed model and another separated lane change control strategy. The experimental results show that the proposed method effectively completes the preceding vehicle switching during the lane change and quickly realizes the stable tracking of the target lane vehicles after changing lanes in a pure CAV traffic environment.