The Optimal Horizon Model Predictive Control Planning for Autonomous Vehicles in Dynamic Environments

A primary challenge in autonomous driving is achieving safe and efficient trajectory planning in complex dynamic environments. This task requires adherence to traffic laws and vehicle dynamics models as well as an understanding of the spatial distributions and behavior of various traffic participants in densely populated areas. Model Predictive Control (MPC) and its variants typically employ a fixed prediction horizon, which results in limited adaptability in dynamic environments. A long prediction horizon escalates computational costs, while a short prediction horizon may impact real-time performance adversely. To tackle this challenge, our study introduces an optimal horizon MPC planning approach. This method incorporates a sliding horizon window founded on reinforcement learning and interactive MPC planning, making it versatile for a variety of driving scenarios. Additionally, our approach implicitly models the spatio-temporal interactions among traffic participants, thereby enriching the information pool for effective planning. Rigorous tests and validations conducted using the real-world dataset nuPlan affirm that our proposed method delivers robust planning performance, facilitating safe and efficient trajectory planning for autonomous vehicles.