複雑な運転シナリオにおけるモデル予測理論に基づく動的軌道計画の研究

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.