Mo, XiaoyuXing, YangLv, Chen2024-11-202024-11-202024-11Mo X, Xing Y, Lv C. (2024) Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction. Transportation Research Part E: Logistics and Transportation Review, Volume 191, November 2024, Article number 1037481366-5545https://doi.org/10.1016/j.tre.2024.103748https://dspace.lib.cranfield.ac.uk/handle/1826/23143Predicting the trajectories of neighboring vehicles is vital for self-driving cars in intricate real-world driving. The challenge lies in accounting for diverse influences on a vehicle's movement, travel needs, neighboring vehicles, and a local map. To address these factors comprehensively, we have developed a framework with a Heterogeneous Graph Social (HGS) pooling approach. The framework represents vehicles and infrastructures in a single graph, with vehicle nodes holding historical dynamics information and infrastructure nodes containing spatial features from map images. HGS captures vehicle–infrastructure interactions in urban driving. Unlike existing methods that are restricted to a fixed vehicle count and highway settings, HGS can accommodate variable interactions and road layouts. By merging all features, our approach predicts the target vehicle's future path. Experiments on real-world data confirm HGS's superiority, boasting quicker training and inference, affirming its feasibility, effectiveness, and efficiency.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/Trajectory predictionConnected vehiclesGraph neural networksHeterogeneous interactions3509 Transportation, Logistics and Supply Chains35 Commerce, Management, Tourism and Services7 Affordable and Clean EnergyLogistics & Transportation3509 Transportation, logistics and supply chainsArticle1878-5794553308103748191