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

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/Referenceless 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. This article discusses the improvements in machine vision recognition of green bike lanes compared to unpainted lanes and details the performance of sensors across varying environmental conditions.