Pedestrian Intention Prediction via Vision-Language Foundation Models

Azarmi, M., Rezaei, M. orcid.org/0000-0003-3892-421X and Wang, H. (2025) Pedestrian Intention Prediction via Vision-Language Foundation Models. In: 2025 IEEE Intelligent Vehicles Symposium (IV). 2025 IEEE Intelligent Vehicles Symposium (IV), 22-25 Jun 2025, Cluj-Napoca, Romania. IEEE , pp. 1899-1904. ISBN: 979-8-3315-3804-0 ISSN: 1931-0587 EISSN: 2642-7214

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Item Type: Proceedings Paper
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Copyright, Publisher and Additional Information:

This is an author produced version of a conference paper published in IEEE Symposium on Intelligent Vehicle Proceedings 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: Visualization, Pedestrians, Accuracy, Systematics, Foundation models, Refining, Vehicle dynamics, Optimization, Autonomous vehicles, Context modeling
Dates:
  • Accepted: 24 April 2025
  • Published (online): 6 August 2025
  • Published: 6 August 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds)
Funding Information:
Funder
Grant number
EU - European Union
101006664
Depositing User: Symplectic Publications
Date Deposited: 29 Jul 2025 13:49
Last Modified: 26 Aug 2025 16:58
Published Version: https://ieeexplore.ieee.org/document/11097349
Status: Published
Publisher: IEEE
Identification Number: 10.1109/IV64158.2025.11097349
Open Archives Initiative ID (OAI ID):

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