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<p>Vulnerable road users (VRUs), such as pedestrians and bicyclists, face a higher risk of severe injuries and fatalities in road collisions, with intersections being particularly hazardous. Enhancing VRU safety at intersections is therefore critical for a safer transportation system. This study introduces a proof-of-concept system capable of detecting VRUs at intersections leveraging image data from vision sensors mounted on roadside infrastructure (e.g., traffic poles). The approach includes the development of a unique VRU detection dataset, comprising labeled images of various VRU types – adults, children, and bicyclists – captured under a range of illumination and weather conditions at real-world public intersections. This dataset addresses a notable gap in VRU detection research, as few datasets offer such environmental diversity from a roadside infrastructure perspective. The dataset was leveraged to train state-of-the-art deep learning models optimized for VRU detection. The models were evaluated using data from both public intersections and a controlled test facility, with particular focus on performance under challenging conditions such as snow and low nighttime visibility. Real-time performance benchmarking of the models was assessed, highlighting their effectiveness in dynamic environments. The results demonstrated that the best model achieved a mean average precision (mAP) of 82% in VRU detection while processing full-HD (1920 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="13177_2025_507_Article_IEq1.gif" Format="GIF" Height="13" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(\times \)</EquationSource> <EquationSource Format="MATHML"><math> <mo>×</mo> </math></EquationSource> </InlineEquation> 1080) frames in real time at 75 ms. Additionally, major challenges in VRU detection at intersections were identified, and recommendations for future research directions were provided.</p>

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Intelligent Infrastructure for Enhancing Vulnerable Road User Safety using Machine Vision Technologies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月24日

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  • Md Atiqur Rahman,
  • Abdelhamid Mammeri,
  • Samy Metari

摘要

Vulnerable road users (VRUs), such as pedestrians and bicyclists, face a higher risk of severe injuries and fatalities in road collisions, with intersections being particularly hazardous. Enhancing VRU safety at intersections is therefore critical for a safer transportation system. This study introduces a proof-of-concept system capable of detecting VRUs at intersections leveraging image data from vision sensors mounted on roadside infrastructure (e.g., traffic poles). The approach includes the development of a unique VRU detection dataset, comprising labeled images of various VRU types – adults, children, and bicyclists – captured under a range of illumination and weather conditions at real-world public intersections. This dataset addresses a notable gap in VRU detection research, as few datasets offer such environmental diversity from a roadside infrastructure perspective. The dataset was leveraged to train state-of-the-art deep learning models optimized for VRU detection. The models were evaluated using data from both public intersections and a controlled test facility, with particular focus on performance under challenging conditions such as snow and low nighttime visibility. Real-time performance benchmarking of the models was assessed, highlighting their effectiveness in dynamic environments. The results demonstrated that the best model achieved a mean average precision (mAP) of 82% in VRU detection while processing full-HD (1920 \(\times \) × 1080) frames in real time at 75 ms. Additionally, major challenges in VRU detection at intersections were identified, and recommendations for future research directions were provided.