Research on Dynamic Trajectory Planning Based on Model Predictive Theory for Complex Driving Scenarios

Autonomous driving, a transformative automotive technology, is currently a major research focus. Trajectory planning, one of the three core technologies for realizing autonomous driving, plays a decisive role in the performance of autonomous driving systems. The key challenge lies in planning an optimal trajectory based on real-time environmental information, yet significant research gaps remain, particularly for dynamic driving scenarios. To address this, our study investigates lane-changing trajectory planning in dynamic scenarios based on model predictive control (MPC) theory and proposes a novel dynamic lane-changing trajectory planning method. First, kinematic models for both the host vehicle and surrounding vehicles are established. Then, following the core components of MPC theory, we construct a prediction model, define an objective function, and formulate constraints for the rolling optimization step. Finally, the optimal control sequence derived from the optimization is processed using a least-squares fitting method to generate a lane-changing trajectory that demonstrates real-time adaptability in dynamic environments. The proposed method is validated through simulation studies of three typical driving conditions on a co-simulation platform. The results confirm that the planned trajectory exhibits excellent dynamic real-time adaptability, thereby contributing a foundation for achieving full-scenario autonomous driving.