Abstract 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.
Intelligent Infrastructure for Enhancing Vulnerable Road User Safety using Machine Vision Technologies
Int. J. ITS Res.
2025-05-24
Article (Journal)
Electronic Resource
English
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