Motion Prediction of Multi-agent systems with Multi-view clustering

TL;DR

This paper introduces a multi-view clustering method for predicting future motions of multi-agent systems by integrating formation, intent, and cost-based clustering, enhanced with an unscented Kalman filter.

Contribution

It proposes a novel clustering approach that combines physics-based metrics and optimal control costs for improved multi-agent motion prediction.

Findings

Effective clustering of agents based on cost metrics.

Improved motion prediction accuracy in simulations.

Adaptive clustering with Kalman filter updates.

Abstract

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.