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arXiv Computer Vision By Fran\c{c}ois G. Landry, Moulay A. Akhloufi

TrajFusionNet+: Transformer-Based Prediction of Pedestrian Crossing Intention via Fusion of Trajectory Representations and Scene Graphs

Read the original on arXiv Computer Vision →

TrajFusionNet+ is a transformer-based model that predicts pedestrian crossing intention by fusing sequential trajectory data, visual trajectory overlays, and graph-based scene context. It extends the earlier TrajFusionNet with three attention modules—Sequence, Visual, and Graph—to capture temporal, visual, and relational cues. The model outperforms state‑of‑the‑art methods on the PIE and JAAD datasets and shows better generalization under a joint‑training, separate‑evaluation protocol.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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