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arXiv · 2401.06344

Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction

Weizheng Wang·Baijian Yang·Sungeun Hong·Wenhai Sun·Byung-Cheol Min
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Abstract

Predicting crowd intentions and trajectories is critical for a range of real-world applications, involving social robotics and autonomous driving. Accurately modeling such behavior remains challenging due to the complexity of pairwise spatial-temporal interactions and the heterogeneous influence of groupwise dynamics. To address these challenges, we propose Hyper-STTN, a Hypergraph-based Spatial-Temporal Transformer Network for crowd trajectory prediction. Hyper-STTN constructs multiscale hypergraphs of varying group sizes to model groupwise correlations, captured through spectral hypergraph convolution based on random-walk probabilities. In parallel, a spatial-temporal transformer is employed to learn pedestrians' pairwise latent interactions across spatial and temporal dimensions. These heterogeneous groupwise and pairwise features are subsequently fused and aligned via a multimodal transformer. Extensive experiments on public pedestrian motion datasets demonstrate that Hyper-STTN consistently outperforms state-of-the-art baselines and ablation models.

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Weizheng Wang, Baijian Yang, Sungeun Hong, Wenhai Sun, Byung-Cheol Min. 2024-01-12. Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction. https://arxiv.org/abs/2401.06344

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