Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework

2026-09-29

AI Summary

This study addresses the computational challenge of efficiently solving decentralized Nash equilibria in non-cooperative games among heterogeneous autonomous vehicles, where interaction asymmetry complicates equilibrium computation. To overcome this, the work proposes an α-potential game framework that explicitly constructs a potential function to reformulate equilibrium seeking as a single-objective optimization problem. By introducing vehicle-specific scaling factors, the approach suppresses interaction asymmetry and quantifies the approximation error, thereby achieving precise equilibrium approximation. The proposed framework provides theoretical guarantees on social efficiency and yields decentralized closed-loop policies. Its flexibility and computational efficiency are validated across complex driving scenarios, including collision avoidance, lane changing, and intersection navigation.

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.

Problem

• Decentralized Decision-Making
• Heterogeneous Autonomous Vehicles
• Noncooperative Game
• Nash Equilibrium
• Asymmetric Interaction

Innovation

• alpha-potential game
• decentralized decision-making
• heterogeneous autonomous vehicles
• approximate Nash equilibrium
• interaction asymmetry