发表机构
University of Southern California（南加州大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于智能体的演化博弈模型，解释混合自主性交织匝道中利他行为如何涌现，并证明其收敛于宏观均衡，为分散式部署提供微观基础。
AI 中文摘要
现有的混合自主性交织匝道模型刻画了利他性互联自动驾驶车辆（CAVs）如何在群体层面提升交通效率，但对于这种行为如何从分散的车辆交互中涌现，以及它如何受到有限种群、异质性偏好和不完全信息的影响，提供的洞察有限。我们开发了一个宏观交织匝道框架的基于智能体模型，其中个体车辆通过演化博弈论更新规则和基于利他性的目标来调整其车道选择，为原始的Wardrop模型提供了微观解释。我们证明了分散动力学收敛到宏观理论预测的唯一均衡。除了再现总体均衡行为外，该框架还使得能够研究静态分析无法解决的部署层面问题。仿真结果与宏观预测高度一致，同时揭示了收敛速率、对变化交通条件的适应、CAVs之间异质性利他水平以及不完全状态信息如何影响系统性能和利他负担在车辆间的分布。这些结果为均衡交通理论与分散式混合自主性部署之间架起了桥梁。
英文摘要
Existing models of mixed-autonomy weaving ramps characterize how altruistic connected and automated vehicles (CAVs) can improve traffic efficiency at the population level, but provide limited insight into how such behavior emerges from decentralized vehicle interactions or how it is affected by finite populations, heterogeneous preferences, and imperfect information. We develop an agent-based model of a macroscopic weaving-ramp framework in which individual vehicles adapt their lane choices using an evolutionary game-theoretic update rule and altruism-based objectives providing a microscopic interpretation of the original Wardrop model. We prove convergence of the decentralized dynamics to the unique equilibrium predicted by the macroscopic theory. Beyond reproducing aggregate equilibrium behavior, the framework enables the study of deployment-level questions that cannot be addressed by static analysis. Simulation results demonstrate close agreement with the macroscopic predictions while revealing how convergence rates, adaptation to changing traffic conditions, heterogeneous altruism levels among CAVs, and imperfect state information influence system performance and the distribution of altruistic burden across vehicles. These results provide a bridge between equilibrium traffic theory and decentralized mixed-autonomy deployment.
Comments6 pages, 9 figures. Accepted for publication at the 6th IFAC Workshop on Cyber-Physical Human Systems (CPHS 2026), December 11-12, 2026, Redondo Beach, CA, USA
