Vehicle Trajectory Planning and Control Based on Bi-Level Model Predictive Control Algorithm

Autonomous driving technology represents a significant direction for future transportation, encompassing four key aspects: perception, planning, decision-making, and control. Among these aspects, vehicle trajectory planning and control are crucial for achieving safe and efficient autonomous driving. This paper introduces a Combined Model Predictive Control algorithm aimed at ensuring collision-free and comfortable driving while adhering to appropriate lane trajectories. Due to the algorithm is divided into two layers, it is also called the Bi-Level Model Predictive Control algorithm (BLMPC). The BLMPC algorithm comprises two layers. The upper-level trajectory planner, to reduce planning time, employs a point mass model that neglects the vehicle's physical dimensions as the planning model. Additionally, obstacle avoidance cost functions are integrated into the planning process. In the upper trajectory planner, the fifth-order polynomial algorithm is also used to smooth the planned trajectory to meet the requirements of vehicle dynamics and passenger comfort. The lower-level trajectory tracker is responsible for real-time trajectory tracking and control, and the paper conducts experiments comparing the Model Predictive Control (MPC) algorithm with the Linear Quadratic Regulator (LQR) algorithm, under the premise of considering the feasibility, cost and safety of the experiment, the front wheel steering Ackerman experimental car is selected as the experimental carrier to verify the reliability of the MPC trajectory tracking control algorithm and ensure the stable driving of the vehicle along the planned trajectory. To address complex road environments, a dynamic obstacle avoidance algorithm is incorporated during the trajectory planning phase. This algorithm allows the vehicle to rapidly generate collision-free trajectories when encountering obstacles, utilizing techniques such as envelope polygonal distance and anti-roll constraints. Compared with other trajectory planning algorithms, the MPC algorithm used in this paper can better adapt to the uncertainty of the system and has better robustness in the face of external disturbances. Finally, the proposed approach is validated through simulation experiments using the Carsim-Simulink co-simulation tool at four different speeds: 10 m/s, 20 m/s, 30 m/s, and 40 m/s. The results demonstrate that the BLMPC algorithm not only ensures safe and comfortable driving but also exhibits high planning efficiency and obstacle avoidance performance in complex road environments. This research provides valuable guidance for advancing autonomous driving technology and its practical implementation