Geometric Graph Neural Network Modeling of Human Interactions in Crowded Environments ⁎

作     者：Sara Honarvar Yancy Diaz-Mercado 

作者机构：Department of Mechanical Engineering University of Maryland College Park MD 20742 USA 

出 版 物：《IFAC-PapersOnLine》 

年 卷 期：2024年第58卷第28期

页      面：25-30页

主　　题：Machine Learning in modeling estimation control Modeling Validation Multi-agent Networked Systems Graph Neural Network Crowd Navigation 

摘      要：Modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using complete graphs, we defne interaction neighborhoods using pedestrians’ field of view, motion direction, and distance-based kernel functions to construct graph representations of crowds. Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics. Our findings underscore the importance of integrating domain knowledge with data-driven approaches for effective modeling of human interactions in crowds.