Modular Decision-Making and Drivable Areas for Multi-Agent Autonomous Racing

Abstract

This paper presents an interaction-aware, modular framework for local trajectory planning in autonomous driving, particularly suited for multi-agent racing scenarios. Our framework first identifies viable drivable areas (tunnels), taking into account predictions of other agents’ behaviors, and subsequently utilizes a high-level decision-making module to select the optimal corridor considering both static and moving vehicles. This decision-making module also strategically determines when to follow an opponent or initiate an overtaking maneuver, while ensuring compliance with racing regulations. A Model Predictive Control (MPC) module is then employed to compute an optimal, collision-free trajectory within the chosen corridor. The proposed modular architecture simplifies the computational complexity typically associated with MPC optimization and facilitates independent component testing. Simulations and real-world tests on various racing tracks demonstrate the efficacy of our approach, even in highly dynamic interactive scenarios with multiple simultaneous opponents; videos of these and additional experiments are available at https://atoschi.github.io/tunnels-framework/.