{"ID":2832560,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.05712","arxiv_id":"2512.05712","title":"$α$-Potential Games for Decentralized Control of Connected and Automated Vehicles","abstract":"Designing scalable and safe control strategies for large populations of connected and automated vehicles (CAVs) requires accounting for strategic interactions among heterogeneous agents under decentralized information. While dynamic games provide a natural modeling framework, computing Nash equilibria (NEs) in large-scale settings remains challenging, and existing mean-field game approximations rely on restrictive assumptions that fail to capture collision avoidance and heterogeneous behaviors. This paper proposes an $α$-potential game framework for decentralized CAV control. We show that computing $α$-NE reduces to solving a decentralized control problem, and derive tight bounds of the parameter $α$ based on interaction intensity and asymmetry. We further develop scalable policy gradient algorithms for computing $α$-NEs using decentralized neural-network policies. Numerical experiments demonstrate that the proposed framework accommodates diverse traffic flow models and effectively captures collision avoidance, obstacle avoidance, and agent heterogeneity.","short_abstract":"Designing scalable and safe control strategies for large populations of connected and automated vehicles (CAVs) requires accounting for strategic interactions among heterogeneous agents under decentralized information. While dynamic games provide a natural modeling framework, computing Nash equilibria (NEs) in large-sc...","url_abs":"https://arxiv.org/abs/2512.05712","url_pdf":"https://arxiv.org/pdf/2512.05712v1","authors":"[\"Xuan Di\",\"Anran Hu\",\"Zhexin Wang\",\"Yufei Zhang\"]","published":"2025-12-05T13:39:32Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.GT\",\"cs.MA\"]","methods":"[]","has_code":false}