Higher-order Relational Reasoning for Pedestrian Trajectory Prediction

Abstract

Social relations have substantial impacts on the potential trajectories of each individual. Modeling these dynamics has been a central solution for more precise and accurate trajectory forecasting. However, previous works ignore the importance of ‘social depth’, meaning the influences flowing from different degrees of social relations. In this work, we propose HighGraph, a graph-based pedestrian relational reasoning method that captures the higherorder dynamics of social interactions. First, we construct a collision-aware relation graph based on the agents’ observed trajectories. Upon this graph structure, we build our core module that aggregates the agent features from diverse social distances. As a result, the network is able to model complex social relations, thereby yielding more accurate and socially acceptable trajectories. Our High- Graph is a plug-and-play module that can be easily applied to any current trajectory predictors. Extensive experiments with ETH/UCY and SDD datasets demonstrate that our HighGraph noticeably improves the previous state-ofthe- art baselines both quantitatively and qualitatively.