PIP-Net: Pedestrian Intention Prediction in the Wild

Azarmi, M. orcid.org/0000-0003-0737-9204, Rezaei, M. orcid.org/0000-0003-3892-421X and Wang, H. (2025) PIP-Net: Pedestrian Intention Prediction in the Wild. IEEE Transactions on Intelligent Transportation Systems, 26 (7). pp. 9824-9837. ISSN 1524-9050

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Item Type: Article
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This is an author produced version of an article published in IEEE Transactions on Intelligent Transportation Systems, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Autonomous vehicles, pedestrian behaviour, pedestrian crossing prediction, computer vision, deep neural networks
Dates:
  • Published (online): 2 June 2025
  • Published: July 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 08 Jul 2025 10:36
Last Modified: 08 Jul 2025 10:36
Status: Published
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Identification Number: 10.1109/tits.2025.3570794
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Sustainable Development Goals:
  • Sustainable Development Goals: Goal 3: Good Health and Well-Being
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