A Hybrid Sampling-Based Trajectory Planner with Game-Theoretic Guidance for Autonomous Racing

2026-07-14

AI Summary

This work addresses the challenge of integrating strategic multi-agent interactions—such as blocking maneuvers—with efficient trajectory planning in autonomous racing under extreme handling conditions. The authors propose a novel hybrid architecture that combines a lightweight game-theoretic mechanism with high-frequency sampling-based planning. Specifically, they offline-learn a potential function derived from α-potential games and, at runtime, employ gradient-based optimization to generate interactive reference trajectories. These trajectories serve as dynamic cost biases that guide the sampling planner. By circumventing the need for online solution of full dynamic games, the approach significantly reduces computational overhead while preserving strategic behavior. High-fidelity simulations on the Yas Marina Circuit demonstrate successful execution of defensive driving strategies, achieving both real-time performance and effective tactical decision-making.

Abstract

Autonomous racing demands planning algorithms that balance vehicle dynamics at the limits of handling with strategic decision-making in competitive multi-agent scenarios. Game theory provides a mathematical framework for modeling these interactions, enabling interactive trajectory planning and strategic behaviors, such as blocking. However, directly solving full dynamic games online is computationally prohibitive and challenging to integrate into robust, high-frequency autonomous software stacks. This paper proposes a hybrid architecture that integrates game-theoretic reasoning into a sampling-based motion planner, combining strategic interactions with robust trajectory generation. Building upon an $α$-potential game formulation, we utilize an offline-learned potential function to capture multi-agent interactions. During online operation, a gradient-based optimization dynamically refines interaction parameters to generate an \textit{Interaction Reference Path}. This path serves as a dynamic cost bias within a high-frequency sampling planner. We evaluate our approach in a high-fidelity simulation environment on the Yas Marina Circuit. Qualitative and quantitative results demonstrate that our approach successfully induces defensive behaviors like blocking without carrying the computational burden of full dynamic game solvers.

Problem

• autonomous racing
• trajectory planning
• game theory
• multi-agent interaction
• computational complexity

Innovation

• game-theoretic planning
• sampling-based trajectory planner
• autonomous racing
• α-potential game
• interaction reference path