Game-Theoretic Autonomous Vehicle Path Coordination with Limited Communication

The coordination of autonomous vehicles in communication-constrained environments is vital for tasks such as search and rescue, maritime surveillance, and disaster response. Traditional path planning approaches often rely on extensive communication to ensure collision avoidance and optimal trajectory selection, which is not feasible in adversarial or bandwidth-limited settings. This paper introduces a game-theoretic framework for multi-vehicle coordination, leveraging Nash Equilibria and approximation algorithms to enable effective decision-making with minimal communication overhead. We explore three limited communication strategies: a $\frac{2}{3}$-Well- Supported Nash Equilibrium (WSNE) algorithm, a $\frac{3}{4}$-WSNE algorithm, and a proposed two-way communication extension based on the $\frac{3}{4}$-WSNE algorithm, dubbed "Two-Way $\frac{3}{4}$-WSNE." These methods are evaluated using both simulated Voronoi roadmaps and real-world experiments with Autonomous Surface Vehicles. Our results demonstrate that strategic path selection can be achieved with minimal information exchange while ensuring collision avoidance and near-optimal path efficiency. This work contributes to the development of scalable, decentralized coordination strategies for autonomous vehicles operating in communication-constrained environments.