Pedestrian Trajectory Prediction Based on Transformer and Multi-relation Graph Convolutional Networks

Abstract

In the field of autonomous navigation, pedestrian trajectories are relatively complex, and accurately predicting pedestrian trajectories is crucial for ensuring safe travel and autonomous driving. Pedestrian trajectories are highly random, dynamic, and influenced by their surroundings, necessitating the effective modeling of their temporal and spatial interactions. To address this, a pedestrian trajectory prediction model combining Transformer and multi-relation graph convolutional network (GCN) is proposed. The model is composed of interaction capture module, anchor control module, and trajectory refinement module. The interaction capture module extracts motion features of each pedestrian on temporal and spatial sequences by using T-Transformer and GCN, while the anchor control module reduces errors by inferring intermediate destinations. The trajectory refinement module enhances predictions. Adding the inverse relationship when extracting features can obtain more optimized results, and using Gaussian pruning to reduce the generation of false paths can also improve the efficiency of the model. Experimental results on ETH and UCY datasets show superior performance in average displacement error (ADE) and final displacement error (FDE) compared with mainstream models. The excellent performance of the model on pedestrian trajectory prediction minimizes unnecessary trajectory changes and collision risks, offering a promising solution for pedestrian trajectory prediction applications.

Keywords

• t-transformer; graph convolutional network (gcn); anchor control; pedestrian trajectory prediction