  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.
