STI-GAN: Multimodal Pedestrian Trajectory Prediction Using Spatiotemporal Interactions and a Generative Adversarial Network

Lei Huang,Hongjie Ma,Riming Xu,Jihui Zhuang,Xiaoming Cheng

doi:10.1109/access.2021.3069134

Abstract

Predicting the future trajectories of multiple pedestrians in certain scenes has become a key task for ensuring that autonomous vehicles, socially interactive robots and other autonomous mobile platforms can navigate safely. The social interactions between people and the multimodal nature of pedestrian movement make pedestrian trajectory prediction a challenging task. In this paper, the problem is solved using a generative adversarial network (GAN) and a graph attention network (GAT) based on the spatiotemporal interaction information about pedestrians. Our method, STI-GAN, is based on an end-to-end GAN model that simulates the pedestrian distribution to capture the uncertainty of the predicted paths and generate more reasonable future trajectories. The complex interactions between people are modeled by a GAT, and spatiotemporal interaction information is used to improve the performance of trajectory prediction. We verify the robustness and improvement of our framework by evaluating its results on various datasets and comparing them with the results of several existing baselines. Compared with the existing pedestrian trajectory prediction methods, our method reduces the average displacement error (ADE) and final displacement error (FDE) by 21.9% and 23.8% respectively.

Highlights

Because of its importance in video monitoring [1], planning and control of automatic driving [2], and robot navigation [3], pedestrian trajectory prediction has long been a popular focus of research in the field of computer vision
Compared with the SGAN model, its average average displacement error (ADE) over the 8 and 12 time steps are reduced by 21.9% and 9.4%, respectively, and the corresponding final displacement error (FDE) are reduced by 23.8% and 22.9%
The ADEs of the STI-generative adversarial network (GAN)-20V-20 model for prediction over 8 and 12 future time steps are reduced by 18.8% and 19.0%, respectively, and the corresponding FDEs are reduced by 8.6% and 14.6%

Summary

Introduction

Because of its importance in video monitoring [1], planning and control of automatic driving [2], and robot navigation [3], pedestrian trajectory prediction has long been a popular focus of research in the field of computer vision. The prediction of pedestrian trajectories in a congested environment still presents many challenges, such as modeling the interactions between pedestrians and the surrounding environment, pedestrian trajectory uncertainty, and the capture of pedestrian intentions. Haddad et al [5] used spatiotemporal graphs to capture both the temporal and spatial correlations of pedestrian predictions and considered physical cues in a scene and the interactions between pedestrians, thereby improving the performance of trajectory prediction. Liang et al [6] and Liu et al [7] considered pedestrianscene and pedestrian-object relationships simultaneously and incorporated pedestrian intentions to model future paths and predict human activities and locations.

Objectives

Methods

Results

Conclusion