GSGFormer: Generative Social Graph Transformer for Multimodal Pedestrian Trajectory Prediction
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Abstract (arXiv)
Pedestrian trajectory prediction, vital for selfdriving cars and socially-aware robots, is complicated due to intricate interactions between pedestrians, their environment, and other Vulnerable Road Users. This paper presents GSGFormer, an innovative generative model adept at predicting pedestrian trajectories by considering these complex interactions and offering a plethora of potential modal behaviors. We incorporate a heterogeneous graph neural network to capture interactions between pedestrians, semantic maps, and potential destinations. The Transformer module extracts temporal features, while our novel CVAE-Residual-GMM module promotes diverse behavioral modality generation. Through evaluations on multiple public datasets, GSGFormer not only outperforms leading methods with ample data but also remains competitive when data is limited.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 7 Dec 2023.
Builds on
30 of 42 references.
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18 more
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