SA-GCNN: Spatial Attention Based Graph Convolutional Neural Network for Pedestrian Trajectory Prediction

Abstract

Accurate predicting the trajectories of moving pedestrians is a key technology in automatic driving system, which is challenging due to the complex interactions in pedestrians. Recent studies have shown that Spatio-Temporal (ST) graph has great ability to capture interactions between pedestrians. However, these methods neglect pedestrian's limited vision and contains many invalid interactions. In order to tackle this issue, we proposed a Spatial Attention based Graph Convolutional Neural Network (SA-GCNN), which uses SA module to construct ST graph and focus on the most useful interactions. Meanwhile, SA-GCNN introduces temporal convolution module to capture temporal dependency between ST graphs. Moreover, the Graph Convolutional Network (GCN) and Temporal Convolution Network (TCN) are combined to extract graph features and decode multi-modal trajectories. Our model is trained on the widely-accepted benchmark datasets ETH and UCY. The empirical findings demonstrate that our SA-GCNN surpasses the performance of existing state-of-the-art methods used for comparison, suggests that our proposed model exhibits enhanced proficiency in capturing pedestrian interactions.