An Overtaking Trajectory Planning Framework Based on Spatio-temporal Topology and Reachable Set Analysis Ensuring Time Efficiency

TL;DR

This paper introduces SROP, a novel overtaking trajectory planning framework that uses topological classes and reachable set analysis to improve efficiency, diversity, and feasibility in high-speed scenarios.

Contribution

It presents a hierarchical planning approach combining topological classification and reachable sets to enhance trajectory diversity and computational speed, addressing local optima issues.

Findings

Improves trajectory smoothness by 66.8%.

Reduces computation time by 62.9%.

Achieves high overtaking success rates in autonomous racing simulations.

Abstract

Generating overtaking trajectories in high-speed scenarios is typically addressed through hierarchical planning, which often suffers from local optima due to single initial solutions and low computational efficiency during numerical optimization. To overcome these limitations, this paper proposes a Spatio-temporal topology and Reachable set analysis enhanced Overtaking trajectory Planning framework (SROP). Specifically, by introducing topological classes to represent distinct overtaking behaviors, the upper-layer planner performs a spatio-temporal search to extract diverse initial paths, effectively preventing local optima. Subsequently, a lower-layer planner conducts parallel trajectory evaluation using reachable sets, which decouples vehicle kinematic constraints from the optimization process to ensure feasibility and significantly accelerate computation. Numerical experiments demonstrate that SROP improves trajectory smoothness by 66.8% and reduces computation time by 62.9% compared to state-of-the-art methods. Furthermore, by seamlessly integrating the method into the F1TENTH autonomous racing simulation platform, a 100-lap sensitivity analysis demonstrates high overtaking success rates in challenging scenarios, thereby validating its practical utility, real-time efficiency, and robustness.