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

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 $$\times $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> 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.