Framework simplifies decision-making for diverse autonomous vehicles
Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework
Computer Science and Game TheoryMultiagent Systems
Summary
Managing multiple autonomous vehicles that interact differently can be complicated, especially when each vehicle has its own goals and preferences. The authors created a mathematical framework that makes it easier to find stable strategies where no vehicle benefits much from changing its plan alone. Their method accounts for uneven influences among vehicles and helps ensure safer, more efficient traffic behaviors like avoiding collisions and managing intersections. They also show that tweaking how vehicles weigh each other's actions can improve coordination.
What this means in practice
- •For autonomous vehicle engineers: Design multi-vehicle control algorithms that achieve near-optimal coordination despite differing vehicle behaviors and priorities.
- •For traffic management system developers: Create intersection and lane change coordination tools that handle vehicles with varied priorities and interaction patterns.
Authors
Anran Hu, Zhexin Wang, Yufei Zhang, Xuan Di
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.