VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis

Aly R. Elkammar, Karim M. Gamaleldin, Catherine M. Elias
1/5/2026
cs.CVcs.AIcs.RO

No confident code implementation found for this paper.

Abstract

Pedestrian Intention prediction is one of the key technologies in the transition from level 3 to level 4 autonomous driving. To understand pedestrian crossing behaviour, several elements and features should be taken into consideration to make the roads of tomorrow safer for everybody. We introduce a transformer / video vision transformer based algorithm of different sizes which uses different data modalities .We evaluated our algorithms on popular pedestrian behaviour dataset, JAAD, and have reached SOTA performance and passed the SOTA in metrics like Accuracy, AUC and F1-score. The advantages brought by different model design choices are investigated via extensive ablation studies.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations

87% match2025

Pedestrian Intention Prediction via Vision-Language Foundation Models

84% match2025

PIP-Net: Pedestrian Intention Prediction in the Wild

83% match2024

Pedestrian Motion Prediction Using Transformer-based Behavior Clustering and Data-Driven Reachability Analysis

83% match2024

Local and Global Contextual Features Fusion for Pedestrian Intention Prediction

83% match2023

Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction

82% match1 repo132024

Cite this paper

@article{elkammar2026vitped,
  title  = {VIT-Ped: Visionary Intention Transformer for Pedestrian Behavior Analysis},
  author = {Aly R. Elkammar and Karim M. Gamaleldin and Catherine M. Elias},
  year   = {2026},
  eprint = {2601.01989},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.01989v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2601.01989.svg)](https://codeofpaper.com/paper/2601.01989)

Discussion