Empirical Analysis on Machine Vision Recognition of Green Bike Lanes for Vulnerable Road Users Safety

<div class="section abstract"><div class="htmlview paragraph">Deliberate modifications to infrastructure can significantly enhance machine vision recognition of road sections designed for Vulnerable Road Users, such as green bike lanes. This study evaluates how green bike lanes, compared to unpainted lanes, enhance machine vision recognition and vulnerable road users safety by keeping vehicles at a safe distance and preventing encroachment into designated bike lanes. Conducted at the American Center for Mobility, this study utilizes a vehicle equipped with a front-facing camera to assess green bike lane recognition capabilities across various environmental conditions including dry daytime, dry nighttime, rain, fog, and snow. Data collection involved gathering a comprehensive dataset under diverse conditions and generating masks for lane markings to perform comparative analysis for training Advanced Driver Assistance Systems. Quality measurement and statistical analysis are used to evaluate the effectiveness of machine vision recognition using metrics, such as Blind/Reference-less Image Spatial Quality Evaluator, Naturalness Image Quality Evaluator, and Entropy-based Image Quality Assessment. The results indicate that green bike lanes are more likely to be recognized by machine vision systems across a wide range of environmental conditions, demonstrating enhanced recognition capabilities. Green lane markings exhibit enhanced visibility and stability, with BRISQUE scores below 82, a median contrast ratio of 17.6, and improved resilience to motion blur and NIQE variations under diverse conditions.</div></div>