Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework
Year: 2026
Type: preprint
Abstract: We study noncooperative multi-vehicle games among heterogeneous autonomous vehicles, where each vehicle adopts a decentralized closed-loop policy based on its own state, and optimizes an objective that depends on other vehicles through potentially asymmetric interaction weights. We develop an $α$-potential game framework that reduces the computation of an approximate Nash equilibrium (NE) to the minimization of a single auxiliary $α$-potential function. We explicitly construct this $α$-potential, establish the existence of its minimizers, and characterize the equilibrium approximation error $α$ in terms of interaction asymmetry. We further introduce vehicle-specific scaling to reduce the effective interaction asymmetry, thereby tightening the equilibrium approximation and, in important cases, recovering an exact NE despite asymmetric interactions. We also derive social-efficiency guarantees for the potential-selected policies, revealing how the interaction structure shapes worst-case efficiency. Numerical experiments demonstrate the flexibility of the framework in capturing heterogeneous vehicle interactions, collision and obstacle avoidance, lane changing and overtaking under different traffic configurations, and priority-based intersection crossing.
Source: arXiv (Cornell University)
Authors: Hu, Anran, Wang, Zhexin, Zhang, Yufei, Di, Xuan
Institutions: Columbia University, Imperial College London
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7215030807