Social-PatteRNN: Socially-Aware Trajectory Prediction Guided by Motion Patterns

TL;DR

Social-PatteRNN is a novel trajectory prediction algorithm that leverages short-term motion patterns to infer social context and improve long-term predictions in human and aircraft navigation scenarios.

Contribution

It introduces a recurrent multi-modal model that encodes short-term motion patterns to enhance trajectory prediction by capturing social and contextual cues.

Findings

Achieves state-of-the-art performance across multiple domains.

Effectively encodes social context from motion patterns.

Improves long-term trajectory prediction accuracy.

Abstract

As robots across domains start collaborating with humans in shared environments, algorithms that enable them to reason over human intent are important to achieve safe interplay. In our work, we study human intent through the problem of predicting trajectories in dynamic environments. We explore domains where navigation guidelines are relatively strictly defined but not clearly marked in their physical environments. We hypothesize that within these domains, agents tend to exhibit short-term motion patterns that reveal context information related to the agent's general direction, intermediate goals and rules of motion, e.g., social behavior. From this intuition, we propose Social-PatteRNN, an algorithm for recurrent, multi-modal trajectory prediction that exploits motion patterns to encode the aforesaid contexts. Our approach guides long-term trajectory prediction by learning to predict short-term motion patterns. It then extracts sub-goal information from the patterns and aggregates it as social context. We assess our approach across three domains: humans crowds, humans in sports and manned aircraft in terminal airspace, achieving state-of-the-art performance.