SRAI-LSTM: A Social Relation Attention-based Interaction-aware LSTM for human trajectory prediction

Abstract

Pedestrian trajectory prediction is one of the important research topics in the field of computer vision and a key technology of autonomous driving system. Walking in groups is a common social behavior in which pedestrians pay more attention to the movements of their companions while walking. Motivated by this idea, we propose a Social Relation Attention-based Interaction-aware LSTM (SRAI-LSTM) to model this social behavior for trajectory prediction. We design a social relation encoder module to capture social relation feature between pedestrians through their relative positions. Afterwards, the social relation features are adopted to acquire social relation attentions among pedestrians. Social interaction modeling is achieved by utilizing social relation attentions to aggregate motion features from neighbor pedestrians. Experimental results on two public pedestrian trajectory datasets (ETH and UCY) demonstrate that our proposed model achieves superior performances compared with state-of-the-art methods on ADE and FDE metrics.