[Submitted on 9 Sep 2026]
Title:TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs
View a PDF of the paper titled TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs, by Fran\c{c}ois G. Landry and 1 other authors
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Abstract:The pedestrian crossing intention task involves predicting whether pedestrians are likely to cross the road from the point of view of an autonomous vehicle. We introduce TrajFusionNet+, a novel transformer-based model for pedestrian crossing intention prediction. TrajFusionNet+ combines sequential and visual representations of pedestrian trajectory with a graph-based representation of the scene context in order to predict pedestrian crossing intention. The proposed architecture builds upon our previous model, TrajFusionNet, and comprises three branches: a Sequence Attention Module (SAM), which processes a sequential representation of past and predicted pedestrian trajectories; a Visual Attention Module (VAM), which utilizes a visual representation of the pedestrian trajectories by overlaying observed and predicted bounding boxes onto scene images; and a Graph Attention Module (GAM), which extracts pedestrian-centric graphs from segmented scene images and captures the relational dependencies between pedestrians and traffic elements. TrajFusionNet+ achieves improved state-of-the-art performance on the two most widely used pedestrian crossing intention datasets, PIE and JAAD. Furthermore, we introduce a new evaluation protocol in which models are trained jointly on the PIE and JAAD datasets but evaluated separately on each. Under this setting, TrajFusionNet+ demonstrates superior generalization compared to existing approaches.
Comments: This work has been submitted to Signal, Image and Video Processing for possible publication
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2609.10806 [cs.CV]
(or arXiv:2609.10806v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.10806
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: François G. Landry [view email] [v1] Wed, 9 Sep 2026 20:17:32 UTC (878 KB)
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