提出异步多智能体决策新方法，提升无车道交通中车辆协同效率。
Conditional Max-Sum for Asynchronous Multiagent Decision Making
- 基于因子图与条件化最大和算法，支持异步变量重分配和分布式通信
- 在无车道交通场景中实现车辆横向对齐的协同决策，性能优于基线方法
- 适合自动驾驶协同控制、动态环境多智能体系统等研究者参考
本文提出一种基于因子图与最大和算法的新型多智能体决策方法，适用于动态环境中的异步变量重分配与分布式消息传递。针对自动驾驶车辆在无车道交通中可通信协作的挑战，设计更贴近现实的通信框架，并提出条件化最大和算法——通过改进消息传递机制，更适应异步场景。该框架将因子图用于车辆的策略性决策（如协调横向对齐），叠加我们设计的基于规则的方法，以结构化表示无车道环境并处理底层车辆控制与安全操作。实验表明，相比无通信的领域特定基线，本方法在强协同需求任务中表现更优，且条件化最大和算法相较标准算法更具适应性。
原文摘要 · Abstract (English)
In this paper we present a novel approach for multiagent decision making in dynamic environments based on Factor Graphs and the Max-Sum algorithm, considering asynchronous variable reassignments and distributed message-passing among agents. Motivated by the challenging domain of lane-free traffic where automated vehicles can communicate and coordinate as agents, we propose a more realistic communication framework for Factor Graph formulations that satisfies the above-mentioned restrictions, along with Conditional Max-Sum: an extension of Max-Sum with a revised message-passing process that is better suited for asynchronous settings. The overall application in lane-free traffic can be viewed as a hybrid system where the Factor Graph formulation undertakes the strategic decision making of vehicles, that of desired lateral alignment in a coordinated manner; and acts on top of a rule-based method we devise that provides a structured representation of the lane-free environment for the factors, while also handling the underlying control of vehicles regarding core operations and safety. Our experimental evaluation showcases the capabilities of the proposed framework in problems with intense coordination needs when compared to a domain-specific baseline without communication, and an increased adeptness of Conditional Max-Sum with respect to the standard algorithm.
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