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Title	Ost-Hgcn: Optimized Spatial-Temporal Hypergraph Convolution Network For Trajectory Prediction
ID_Doc	41042
Authors	Lin X.; Zhang Y.; Wang S.; Hu Y.; Yin B.
Year	2025
Published	IEEE Transactions on Intelligent Transportation Systems, 26, 3
DOI	http://dx.doi.org/10.1109/TITS.2025.3529666
Abstract	Pedestrian trajectory prediction is a key component for various applications that involve human and vehicle interactions, such as autonomous driving, traffic management and smart city planning. Existing methods based on graph neural networks have limited ability to capture group interactions and precisely model complex associations among multi-agents. To solve these problems, we propose OST-HGCN, an optimized hypergraph convolutional network. It models multi-agent trajectory interactions from both temporal and spatial perspectives using hypergraph structures, and optimizes the spatio-temporal hypergraph structure to enable fine-grained analysis of multi-agent trajectory motion intentions and high-order interactions. We employ OST-HGCN to a CVAE-based prediction framework, and use the optimized hypergraph structure to predict multi-agent plausible trajectories. We conduct extensive experiments on four real trajectory prediction datasets of NBA, NFL, SDD and ETH-UCY, and verify the effectiveness of the proposed OST-HGCN. © 2000-2011 IEEE.
Author Keywords	hypergraph convolution network; hypergraph structure optimization; multi-agent interaction modeling; Trajectory prediction

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