[2401.06344] Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction

Computer Science > Computer Vision and Pattern Recognition arXiv:2401.06344 (cs) [Submitted on 12 Jan 2024 (v1), last revised 20 Mar 2026 (this version, v4)] Title:Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction Authors:Weizheng Wang, Baijian Yang, Sungeun Hong, Wenhai Sun, Byung-Cheol Min View a PDF of the paper titled Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction, by Weizheng Wang and 4 other authors View PDF HTML (experimental) 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 m...