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.

翻译：本文提出了一种通过考虑群体形成信息和未来意图来实现多智能体系统未来运动预测的方法。群体形成基于一种遵循凝聚层次聚类算法的物理聚类方法。我们识别出包含相关最优控制问题的最小代价函数作为智能体间分组聚类的度量标准，其中具有相似关联代价的群体被认为可能协同运动。该代价度量既考虑了与其他智能体的临近性，也兼顾了各智能体的预期目标。基于无迹卡尔曼滤波的方法被用于更新已建立的聚类，并在获取新信息时添加新聚类。通过在不同场景和智能体数据集上实施所提算法的非平凡数值仿真，验证了本方法的有效性。