New Approach to Intelligent Pedestrian Detection and Signaling on Crosswalks — (Supplementary Material)

The supplementary dataset supporting this research (10.1109/TITS.2024.3445156) provides resources for replicating the experimental evaluation of an intelligent crosswalk system. It includes XLS files with raw and processed photogrammetric data, such as vehicle speed profiles, pedestrian trajectory dispersion and statistical indicators under different lighting conditions (day and night). Additionally, the dataset contains 2,191 MP4 video files with a total size of 15.8 GB, documenting real-world trials over several days. These videos capture traffic scenarios, including pedestrian crossings and vehicle interactions, enabling visual analysis of speed reduction and trajectory improvements. Together, these resources offer a solid foundation for reproducibility, behavioral studies and future research on AI-based traffic signaling systems. No data related to AI algorithms is included due to intellectual property protection under patent WO 2018/050945.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-08-29
DOI
https://doi.org/10.5281/zenodo.17979705
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

New Approach to Intelligent Pedestrian Detection and Signaling on Crosswalks — (Supplementary Material)

Zenodo (CERN European Organization for Nuclear Research)
Autonomous Vehicle Technology and Safety
article

New Approach to Intelligent Pedestrian Detection and Signaling on Crosswalks — (Supplementary Material)

article en

Abstract

The supplementary dataset supporting this research (10.1109/TITS.2024.3445156) provides resources for replicating the experimental evaluation of an intelligent crosswalk system. It includes XLS files with raw and processed photogrammetric data, such as vehicle speed profiles, pedestrian trajectory dispersion and statistical indicators under different lighting conditions (day and night). Additionally, the dataset contains 2,191 MP4 video files with a total size of 15.8 GB, documenting real-world trials over several days. These videos capture traffic scenarios, including pedestrian crossings and vehicle interactions, enabling visual analysis of speed reduction and trajectory improvements. Together, these resources offer a solid foundation for reproducibility, behavioral studies and future research on AI-based traffic signaling systems. No data related to AI algorithms is included due to intellectual property protection under patent WO 2018/050945.

Zenodo (CERN European Organization for Nuclear Research)
Universidad de Huelva (ES)
Openalex Percentile: Top 58%
Autonomous Vehicle Technology and Safety
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New Approach to Intelligent Pedestrian Detection and Signaling on Crosswalks — (Supplementary Material) · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS