RDGCN: Reasonably dense graph convolution network for pedestrian trajectory prediction

Abstract

The pedestrian trajectory prediction remains challenging due to its uncertainty and interference from surrounding pedestrians. There are two deficiencies in previous pedestrian trajectory prediction methods: 1. The temporal correlation of social interaction and the movement factors of groups are ignored; 2. Unreasonable interaction weight allocation. In order to eliminate these two deficiencies, a reasonably dense graph convolution network (RDGCN) was developed in this study. Spatial and temporal graphs were first constructed to model social interactions and movement factors. Then, asymmetric three-dimensional (3D) convolution was employed for the fusion of spatial-temporal information to capture the temporal correlation of social interactions and the movement factors of groups. The RSigmoid function was designed to assign interaction weights and to make the setting of interaction weight more reasonable. Finally, a U-TCN module was designed to estimate two-dimensional Gaussian distribution parameters of the future trajectories. On the ETH and UCY datasets, the proposed method outperformed versus other models in terms of average displacement error and final displacement error, and it was capable of predicting complex social behaviors and movement factors.