Regulation-Aware Game-Theoretic Motion Planning for Autonomous Racing
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.
Cite
@article{arxiv.2508.20203,
title = {Regulation-Aware Game-Theoretic Motion Planning for Autonomous Racing},
author = {Francesco Prignoli and Francesco Borrelli and Paolo Falcone and Mark Pustilnik},
journal= {arXiv preprint arXiv:2508.20203},
year = {2025}
}
Comments
Accepted for presentation at the IEEE International Conference on Intelligent Transportation Systems (ITSC 2025)