Robust Spatiotemporal Motion Planning for Multi-Agent Autonomous Racing via Topological Gap Identification and Accelerated MPC

TL;DR

This paper introduces a robust spatiotemporal planning framework for multi-agent autonomous racing that combines topological gap identification with accelerated MPC, improving safety, efficiency, and computational speed.

Contribution

It presents a novel framework integrating topological gap detection with accelerated MPC for high-speed multi-agent racing, ensuring feasibility and robustness under computational constraints.

Findings

Reduces maneuver time by 51.6% in sequential scenarios

Maintains over 81% overtaking success rate in dense environments

Lowers computational latency by 20.3%

Abstract

High-speed multi-agent autonomous racing demands robust spatiotemporal planning and precise control under strict computational limits. Current methods often oversimplify interactions or abandon strict kinematic constraints. We resolve this by proposing a Topological Gap Identification and Accelerated MPC framework. By predicting opponent behaviors via SGPs, our method constructs dynamic occupancy corridors to robustly select optimal overtaking gaps. We ensure strict kinematic feasibility using a Linear Time-Varying MPC powered by a customized Pseudo-Transient Continuation (PTC) solver for high-frequency execution. Experimental results on the F1TENTH platform show that our method significantly outperforms state-of-the-art baselines: it reduces total maneuver time by 51.6% in sequential scenarios, consistently maintains an overtaking success rate exceeding 81% in dense bottlenecks, and lowers average computational latency by 20.3%, pushing the boundaries of safe and high-speed autonomous racing.