Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An α-Potential Game Framework
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.
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BibTeX
@misc{hu2026decentralized,
title = {{Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework}},
author = {Anran Hu and Zhexin Wang and Yufei Zhang and Xuan Di},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.38731},
eprint = {2609.38731},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.38731},
}
RIS
TY - UNPB
TI - Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An α-Potential Game Framework
AU - Anran Hu
AU - Zhexin Wang
AU - Yufei Zhang
AU - Xuan Di
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.38731
UR - https://arxiv.org/abs/2609.38731
AB - 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.
ER -