Hierarchical secure trajectory planning for cooperative mandatory lane changes

Purpose – This paper aims to develop a cooperative framework for secure trajectory planning of intelligent connected vehicles (ICVs) in mandatory lane change scenarios. Design/methodology/approach – A hierarchical framework is proposed, consisting of a trajectory generation layer and a kinematic execution layer. The trajectory generation layer uses quintic polynomials to parameterize candidate paths and formulates an optimization problem with comfort and efficiency as objectives, subject to constraints including vehicle dynamics, road limits and collision avoidance. Optimal reference trajectories are solved through parallel optimization. The kinematic execution layer adopts a model predictive control scheme to ensure kinematic feasibility, minimizing the deviation from the reference trajectory. Findings – Simulation results demonstrate the effectiveness of the proposed framework in achieving a balance between safety, comfort and efficiency during mandatory lane changes. It maintains high average speeds with minimal fluctuations, ensuring efficiency and comfort, while the adaptive safety boundary mechanism reduces collision risks in dynamic interactions, increasing the success rate in complex traffic. Originality/value – The hierarchical cooperative framework decouples geometric planning and kinematic execution while integrating an adaptive safety boundary mechanism, ensuring both safety and flexibility. This provides an effective solution for secure trajectory planning of ICVs in cooperative mandatory lane change scenarios.