基于图神经网络的行人轨迹预测研究综述

A survey of pedestrian trajectory prediction based on graph neural network

随着计算机视觉和自动驾驶技术的快速发展,自动感知、理解和预测人类行为的能力变得越来越重要。各类传感器的普及使得社会中产生了大量运动物体的位置数据。基于这些数据预测行人的运动轨迹在社交预测等多个领域都有着极大的价值。为了深入了解这方面的发展,对基于图神经网络的行人轨迹预测方法进行了综述,从多个角度比较、分析和总结了行人轨迹预测的图神经网络算法,讨论了不同算法在该领域的研究与发展;在目前的公共数据集上进行了对比和分析,介绍了相应性能指标,给出了不同算法的性能比较结果,提出了目前研究仍存在的问题,拓展研究思路和方法;展望了未来可能出现的研究方向。

With the rapid development of the technology of computer vision and autonomous driving,the ability to sense,understand and predict human behavior is becoming more and more important.The popularity of various sensors has generated a large amount of position data of moving objects in society.Predicting the movement trajectory of pedestrians based on these data has great value in social prediction and other fields.To gain insight into the development in this area,a literature review is conducted on graph neural network-based pedestrian trajectory prediction methods.The graph neural network algorithms for pedestrian trajectory prediction are compared,analyzed and summarized from multiple perspectives,and the research and development of different algorithms in this field are discussed.The comparison and analysis are carried out on the current public data sets,an overview of the corresponding performance indicators is provided,and the performance comparison results of different algorithms are given.At the same time,this paper puts forward the research problems that still exist and looks forward to the possible research directions in the future.