{
  "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.38731",
  "author_total": 4,
  "authors": [
    {
      "name": "Anran Hu"
    },
    {
      "name": "Zhexin Wang"
    },
    {
      "name": "Yufei Zhang"
    },
    {
      "name": "Xuan Di"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bhrkq0t5ke4cx.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.38731",
  "fulltext_url": null,
  "id": "bhrkq0t5ke4cx",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.38731",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.GT"
    ],
    [
      "arxiv",
      "cs.MA"
    ],
    [
      "arxiv",
      "math.OC"
    ],
    [
      "openalex",
      "2207|Traffic control and management"
    ]
  ],
  "openalex": "W7215030807",
  "pdf_url": "https://arxiv.org/pdf/2609.38731",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bhrkq0t5ke4cx.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:49Z",
      "native_id": "2609.38731",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.38731"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:math.OC",
      "subfield": "optimisation-control"
    }
  ],
  "title": "Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An $α$-Potential Game Framework",
  "title_text": "Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An α-Potential Game Framework",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:49Z",
  "url": "https://research.draconitic.com/paper/bhrkq0t5ke4cx",
  "venue": "arXiv",
  "versions": []
}