Trajectory Forecasting Using Graph Convolutional Neural Networks Based on Prior Awareness and Information Fusion

Abstract

Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving, and social robots. The trajectory prediction task is influenced by many factors, including individual historical trajectory, interactions between agents, and the fuzzy nature of an agent’s motion. While existing methods have made great progress on the topic of trajectory prediction, a lot of trajectory prediction methods take into account all pedestrians in the scene when simply modeling the influence of nearby pedestrians, and this inevitably brings redundant information. We propose a pedestrian trajectory prediction model based on prior awareness and information fusion. To make the input information more effective, for the different levels of importance of input trajectory information, we design a time information weighting module to weigh the observed trajectory information differently at different moments based on the original observed trajectory information. To reduce the impact of redundant information on trajectory prediction and to improve interaction between pedestrians, we present a spatial interaction module of multi-pedestrians and a topological graph fusion module. In addition, we use a temporal convolutional network module to obtain the temporal interactions between pedestrians. Compared to Social-STGCNN, the experimental results show that the model we propose reduces the average displacement error (ADE) and final displacement error (FDE) by 32% and 38% in the datasets of ETH and UCY, respectively. Moreover, based on this model, we design an autonomous driving obstacle avoidance system that can effectively ensure the safety of road pedestrians.