Driving impairment detection due to sun exposure and contrasting shadow of surface objects: an urban case study

Abstract

A change in drivers’ vision quality might result in the deterioration of their perception and their effectiveness. The lack of an integrated algorithm to distinguish the location of drivers’ vision impairment motivates this study. The proposed model benefits from a combination of several sub-algorithms, such as sun positioning, glare detection, and contrasting shadow illustrator derived from raw geospatial data. The methodology is implemented through a case study involving a large-size metropolitan area road network, a Digital Elevation Model, the associated hillshade geographic data, and weather data from Montreal, Canada. The methodology and corresponding data analysis are implemented in Python. The result revealed a geospatial model to estimate the boundaries of transition points between the glare and contrasting shadows created by changes in roadway surroundings. The results provided by the model can be used as a tool to aid decision-makers in the new road construction and urban planning by creating safety countermeasure strategies and design review of road geometry.