#7295. STENet: A hybrid spatio-temporal embedding network for human trajectory forecasting

September 2026publication date
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Journal’s subject area:
Control and Systems Engineering;
Electrical and Electronic Engineering;
Artificial Intelligence;
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Abstract:
In this paper, we present a hybrid spatio-temporal embedding network for human trajectory forecasting, which is built upon a hierarchical framework. We propose a two-stage graph attention mechanism, which can better describe mutual interactions among pedestrians in the crowd. Additionally, group influences at every time step are taken into account as well. The overall framework is designed using a hierarchical manner, and trained using the Wasserstein distance.
Keywords:
1D-CNN; Graph attention mechanism; Hierarchical structure; Pedestrian grouping strategy; Trajectory forecasting; Wasserstein distance

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