Hierarchical Time-Optimal Planning for Multi-Vehicle Racing
This work addresses the need for efficient real-time planning in multi-vehicle racing, offering a practical solution that reduces computational requirements without sacrificing performance.
The paper proposes a hierarchical planning algorithm for multi-vehicle racing that combines behavioral planning with time-optimal control, achieving performance comparable to parallel optimization methods while requiring only a single core, thus reducing computational demands.
This paper presents a hierarchical planning algorithm for racing with multiple opponents. The two-stage approach consists of a high-level behavioral planning step and a low-level optimization step. By combining discrete and continuous planning methods, our algorithm encourages global time optimality without being limited by coarse discretization. In the behavioral planning step, the fastest behavior is determined with a low-resolution spatio-temporal visibility graph. Based on the selected behavior, we calculate maneuver envelopes that are subsequently applied as constraints in a time-optimal control problem. The performance of our method is comparable to a parallel approach that selects the fastest trajectory from multiple optimizations with different behavior classes. However, our algorithm can be executed on a single core. This significantly reduces computational requirements, especially when multiple opponents are involved. Therefore, the proposed method is an efficient and practical solution for real-time multi-vehicle racing scenarios.