GSTA: Pedestrian trajectory prediction based on global spatio-temporal association of graph attention network

Abstract

Most encoder-decoder structure based predictions models usually predict trajectory according to the position and historical movement of nearby pedestrians. Their input range (receptive field) is small. They often ignore some specific information such as the speed and direction of pedestrians’ movement or the temporal attention. This leads to detailed pedestrian interaction that cannot be obtained. Therefore, we propose a novel spatio-temporal graph attention network (GAT) called GSTA. In the spatial domain, GSTA obtains complex interaction by spatial attention (SA) based on multi-feature fusion, and expands the receptive field through feature updating mechanism (FUM). In the temporal domain, we design temporal attention module (TAM) and feature selection module (FSM). TAM is used to discover the internal relationship of historical trajectory and solve the problem that temporal attention is averaged. FSM overcomes the adverse effect of small temporal perceptual range and reasonably controls the flow of feature information. Experimental results on 5 commonly used pedestrian trajectory datasets show that the prediction accuracy of our proposed model is further improved.