基于图神经网络的行人轨迹预测研究综述OA

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.