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Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework — arXiv:2609.38731

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.38731
License (as archived): CC0-1.0
Archived: 2026-10-07T12:39:38.136Z
SHA-256 of the archived text: 8c4d5fc358aff429d6faf6a5ebfa51bf25e0dc52f51a1f0db30eb2e2fcc0578c
Stable URL of this page: https://metraipolis.com/documento/dc_cfd2f3722efa

How to cite

Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework — arXiv:2609.38731 — arXiv (en), https://arxiv.org/abs/2609.38731 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cfd2f3722efa (Metraipolis archive dc_cfd2f3722efa, sha256 8c4d5fc358aff429d6faf6a5ebfa51bf25e0dc52f51a1f0db30eb2e2fcc0578c)

BibTeX

@misc{dccfd2f3722efa,
  title = {Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework — arXiv:2609.38731},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.38731},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cfd2f3722efa; sha256 8c4d5fc358aff429d6faf6a5ebfa51bf25e0dc52f51a1f0db30eb2e2fcc0578c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.38731. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 $\alpha$-potential game framework that reduces the computation of an approximate Nash equilibrium (NE) to the minimization of a single auxiliary $\alpha$-potential function. We explicitly construct this $\alpha$-potential, establish the existence of its minimizers, and characterize the equilibrium approximation error $\alpha$ 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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-01 in cs.MA).
Categorie arXiv: Optimization and Control (math.OC) ; Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2609.38731 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T12:12:18.644Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).