This paper presents a method for future motion prediction of multi-agent systems by including group formation information and future intent. Formation of groups depends on a physics-based clustering method that follows the agglomerative hierarchical clustering algorithm. We identify clusters that incorporate the minimum cost-to-go function of a relevant optimal control problem as a metric for clustering between the groups among agents, where groups with similar associated costs are assumed to be likely to move together. The cost metric accounts for proximity to other agents as well as the intended goal of each agent. An unscented Kalman filter based approach is used to update the established clusters as well as add new clusters when new information is obtained. Our approach is verified through non-trivial numerical simulations implementing the proposed algorithm on different datasets pertaining to a variety of scenarios and agents.

中文翻译：

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多视图聚类多智能体系统的运动预测

本文提出了一种通过包含群体形成信息和未来意图来预测多智能体系统未来运动的方法。组的形成取决于遵循凝聚层次聚类算法的基于物理的聚类方法。我们确定了包含相关最优控制问题的最小移动成本函数的集群，作为代理之间群体之间聚类的度量，其中具有相似相关成本的群体被假设为可能一起移动。成本指标考虑了与其他代理的接近程度以及每个代理的预期目标。基于无迹卡尔曼滤波器的方法用于更新已建立的聚类，并在获得新信息时添加新聚类。我们的方法通过在与各种场景和代理相关的不同数据集上实施所提出的算法的重要数值模拟来验证。