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

TL;DR

A Topological Gap Identification and Accelerated MPC framework is proposed that significantly outperforms state-of-the-art baselines and ensures strict kinematic feasibility using a Linear Time-Varying MPC powered by a customized Pseudo-Transient Continuation solver for high-frequency execution.

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.

Figures (6)

• Figure 1: Framework of the proposed Topological Gap Identification Planner(Topo-Gap) : The method first employs parallel to predict multi-opponent spatiotemporal corridors. Based on these dynamic predictions, a topology-aware module identifies the optimal overtaking gap, while a PTC-accelerated optimizes the trajectory to ensure strict kinematic feasibility and safety at high speeds.
• Figure 2: Qualitative visualization of the multi-opponent behavior prediction via parallel SGPs. The models accurately infer the continuous lateral deviation and velocity profiles, generating variance-inflated spatial bounds to support the downstream dynamic corridor construction.
• Figure 3: Illustration of the topology-aware gap selection and adaptive trajectory generation. The ego vehicle (blue) navigates a continuous safe corridor (green) to cleanly bypass the opponents' Spatiotemporal Occupancy Corridors (SOCs, red hatched areas) utilizing a three-phase geometric path.
• Figure 4: Overtaking maneuvers demonstrated in the F1TENTH RViz simulation environment across scenarios (b), (c), and (d). By utilizing the proposed TopoGap planner, the ego vehicle efficiently identifies navigable gaps (indicated by long rectangles) among the opponents' SOCs(denoted by short rectangles) and generates an optimal, smooth trajectory to safely execute the overtakes.
• Figure 5: Qualitative overview of the proposed Topo-Gap framework executing a multi-opponent overtaking maneuver under the SMP scenario. (a) At $t_1$, the ego vehicle (magenta) trails Opponent 1 (gray) and evaluates the spatial gap for an outside overtake. (b) At $t_2$, the ego vehicle successfully clears the first opponent at the curve apex. (c) At $t_3$, it approaches Opponent 2 (teal) while continuously projecting spatiotemporal occupancy corridors. (d) At $t_4$, a safe and smooth inside overtake is completed. The generated trajectories strictly maintain kinematic feasibility without decision oscillation during the highly dynamic multi-agent interactions.