Abstract The advancement of connected autonomous vehicles (CAVs) enables cooperative decision-making for traffic efficiency and safety. This study proposes a centralized lane-change decision coordination framework for multiple CAVs on highways, extending cooperative driving beyond traditional car-following strategies. The controller jointly optimizes speed adaptation and lane-change decisions over a finite prediction horizon to maximize overall traffic utility while ensuring collision-free maneuvers. Assuming reliable vehicle-to-infrastructure (V2I) communication, the planner computes acceleration, braking, and lane-change commands for all vehicles simultaneously. The underlying decision process is formulated as a mixed-integer optimization problem, which is computationally prohibitive for real-time deployment. To address this challenge, a priority-aware search strategy is developed to evaluate only the most promising lane-change combinations at each time step, integrated within a Model Predictive Control (MPC) framework enhanced with Artificial Potential Fields (APFs) for safety assurance and motion guidance. Simulation results demonstrate that the proposed framework effectively balances safety, mobility, and system-level coordination while achieving real-time feasibility through significantly reduced computational complexity. The approach offers a scalable solution for future intelligent transportation infrastructures and real-time traffic management applications. Similar content being viewed by others Funding This work is supported by the German Academic Exchange Service (DAAD) under Grant number 57610577. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission