Hierarchical Time-Optimal Planning for Multi-Vehicle Racing

TL;DR

This paper introduces a hierarchical planning algorithm for multi-vehicle racing that combines behavioral and optimal control methods to achieve near real-time, time-optimal trajectories with reduced computational load.

Contribution

The novel hierarchical approach efficiently integrates discrete behavioral planning with continuous optimization, enabling real-time multi-vehicle racing with high performance.

Findings

Performance comparable to parallel optimization methods

Reduced computational requirements for multi-vehicle scenarios

Capable of real-time execution on a single core

Abstract

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.