Prignoli, Francesco (Università di Modena e Reggio Emilia), Borrelli, Francesco (University of California, Berkeley), Falcone, Paolo (Chalmers University of Technology), Pustilnik, Mark (UC Berkeley)

Regulation-Aware Game-Theoretic Motion Planning for Autonomous Racing

Scheduled for presentation during the Regular Session "S40b-Cooperative and Connected Autonomous Systems" (FR-EA-T40), Friday, November 21, 2025, 13:50−14:10, Cooleangata 4

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

This paper presents a regulation-aware motion planning framework for autonomous racing scenarios. Each agent solves a Regulation-Compliant Model Predictive Control problem, where racing rules—such as right-of-way and collision avoidance responsibilities—are encoded using Mixed Logical Dynamical constraints. We formalize the interaction between vehicles as a Generalized Nash Equilibrium Problem (GNEP) and approximate its solution using an Iterative Best Response scheme. Building on this, we introduce the Regulation-Aware Game-Theoretic Planner (RA-GTP), in which the attacker reasons over the defender’s regulation-constrained behavior. This game-theoretic layer enables the generation of overtaking strategies that are both safe and non-conservative. Simulation results demonstrate that the RA-GTP outperforms baseline methods that assume non-interacting or rule-agnostic opponent models, leading to more effective maneuvers while consistently maintaining compliance with racing regulations.