## 5 Conclusions  
This study proposes a CLC control strategy under a pure CAV traffic flow on a two-lane highway segment. It considers the correlation between the longitudinal and lateral motion of the lane change and the preceding vehicles switching problems of the controlled vehicles. The proposed CLC control strategy divides the lane-changing process into LC-O and LC-T stages based on the lateral position of the lane-changing vehicle. This effectively achieves the specific objective division of different stages of the lane change and reduces the complexity of the model solution. A preceding vehicle switching method using a set of linear piecewise functions based on a given lateral motion is designed to provide a smooth transfer when the preceding vehicles of the controlled vehicle changes. The proposed CLC control strategy is validated by comparing the CACC_Sine control strategy and with regard to the two typical lane change scenarios. The results show the proposed CLC control strategy has a good effect on vehicle switching tracking and can quickly realize the stable tracking of the target lane vehicles after changing lanes. Due to the real-time rolling optimization feature of MPC, the proposed CLC control strategy can also be applied to the scenario where the motion states of the preceding vehicles of two lanes change dynamically. Besides, the good operating efficiency makes the proposed CLC control strategy possible to be integrated into embedded control applications.  
Figure 16: The execution time within each execution cycle of the proposed CLC control strategy for the entire simulation time in two lane change scenarios. (**a**) Scenario 1, (**b**) Scenario 2.  
Figure 17: The memory usage within each execution cycle of the proposed CLC control strategy for the entire simulation time in two lane change scenarios. (**a**) Scenario 1, (**b**) Scenario 2.  
Firstly, the study scenario is based on pure CAV traffic flow and assumes that the motion states of leading vehicles on both lanes are known which is unrealistic the relevant research on the motion states of preceding vehicles will be explored in future work. Secondly, although the correlation between the movement of the two directions is considered in the model construction process, the lateral motion determined by the given trajectory function restricts the applicability of the model. In the future, we will consider constructing a preceding vehicle switching control strategy based on dynamic lane change lateral trajectories. Thirdly, the proposed CLC control strategy in this study is based on deterministic MPC, and uncertainties such as model prediction errors and control delays have not been considered. The introduction of these uncertainty factors in the model construction process will be considered in future work to make the model have better dynamic adaptability and robustness. Lastly, to further improve the efficiency of algorithm execution, we will do some memory optimization for the existing equipment and further optimize the algorithm code to keep the memory usage of the algorithm execution within the memory limit of the hardware equipment.  
Conceptualization, K.S., X.Z. and S.G.; methodology, K.S. and S.G.; validation, K.S. and X.W.; writing--original draft preparation, K.S.; writing--review and editing, K.S., S.G. and X.W.; funding acquisition, X.Z., S.G. and X.W. All authors have read and agreed to the published version of the manuscript.  
This research is funded by National Key R&D Program of China (2019 YFB1600100), NSFC (71901038), the 111 Project on Information of Vehicle-Infrastructure Sensing and ITS (B14043), Joint Laboratory for Internet of Vehicles (213024170015), Shaanxi Province Science Foundation (2020)Q-392, 2022)Q-663), China Postdoctoral Science Foundation (2022M710483), and Research funds for the Central Universities, Chang'an University (300102240301, 300102242103).  
Not applicable.  
The authors are grateful to the reviewers' efforts and their valuable comments.  
The authors declare no conflict of interest.