Dynamic Trajectory Planning and Tracking Based on Lane-Change Time Optimization

With the emergence of global traffic problems, the development of safe, efficient, and reliable intelligent driving technologies has become a research hotspot. As a key component of intelligent driving technology, trajectory planning directly affects the safety, comfort, and operational efficiency of vehicles in complex traffic scenarios. Existing research typically relies on high-dimensional iterative numerical optimization or tightly coupled planning and control structures, leading to high computational complexity, insufficient real-time performance, and difficulty in ensuring trajectory smoothness. To address these issues, this paper proposes a decoupled and integrated trajectory planning and control method. Firstly, a method is proposed to construct the lateral trajectory based on a fifth-order polynomial and generate the longitudinal motion based on a quadratic acceleration model. Then, lane-change time is introduced as a single optimization variable to construct a cost function that balances comfort and efficiency, and continuous optimization is performed under longitudinal safety distance constraints. Finally, a horizontal and longitudinal hierarchical structure is constructed through model predictive control to solve the direction and speed adjustment problems and achieve high-precision tracking of the optimal trajectory. To verify the effectiveness of the proposed method, coupled simulation verification of trajectory generation and vehicle dynamic response is performed based on a joint simulation platform of MATLAB/Simulink and Carsim. The simulation results show that the proposed method can generate smooth, efficient, and controllable overtaking trajectories; significantly reduce computational complexity; and meet safety constraints, thus verifying the feasibility of the proposed method in complex lane-changing scenarios.