计算机科学与探索2026，Vol.20Issue(4)：923-942,20.DOI:10.3778/j.issn.1673-9418.2507052
基于图神经网络的行人轨迹预测研究综述
Review of Pedestrian Trajectory Prediction Based on Graph Neural Networks
摘要
Abstract
Pedestrian trajectory prediction is a critical technology in scenarios such as autonomous driving and intelligent surveillance,where its accuracy directly impacts the decision-making quality of intelligent systems.Traditional methods have certain limitations in effectively modeling the complex social interactions and environmental factors influencing pedestrian movement.Graph neural networks(GNNs),with their strong relational modeling capabilities,have become an important tool in pedestrian trajectory prediction and have demonstrated significant advantages in various predictive tasks.This review systematically organizes GNN-based pedestrian trajectory prediction methods,categorizing them into three types based on model architecture:methods based on spatio-temporal graph neural networks,methods combining GNNs with generative models,and methods integrating GNNs with Transformers.The paper provides an in-depth analysis of the modeling mechanisms,advantages,and limitations of each approach,and summarizes the performance of several representative methods on public datasets.The challenges currently faced by the field are discussed,and potential future research directions are proposed.关键词
行人轨迹预测/图神经网络/时空图神经网络/生成式网络/TransformerKey words
pedestrian trajectory prediction/graph neural networks/spatio-temporal graph neural networks/generative network/Transformer分类
信息技术与安全科学引用本文复制引用
杜婷,庄旭菲,王玉杰,黎子珩,吕洁,智媛媛,赵宇鹏..基于图神经网络的行人轨迹预测研究综述[J].计算机科学与探索,2026,20(4):923-942,20.基金项目
内蒙古自治区科技计划项目(2020GG0104) （2020GG0104）
内蒙古自然科学基金(2023MS06021) （2023MS06021）
内蒙古自治区重点研发和成果转化计划项目(2025YFHH0115).This work was supported by the Science and Technology Program of Inner Mongolia Autonomous Region(2020GG0104),the Natural Science Foundation of Inner Mongolia(2023MS06021),and the Key Research and Development and Achievement Transformation Program of Inner Mongolia Autonomous Region(2025YFHH0115). （2025YFHH0115）
