  The sun glare is one of the major environmental hazards that cause traffic
accidents. Every year, many people died and injured in traffic accidents
related to sun glare. Providing accurate information about when and where sun
glare happens would be helpful to prevent sun glare caused traffic accidents
and save lives. In this study, we proposed to use publicly accessible Google
Street View (GSV) panorama images to estimate and predict the occurrence of sun
glare. GSV images have view sight similar to drivers, which would make GSV
images suitable for estimating the visibility of sun glare to drivers. A
recently developed convolutional neural network algorithm was used to segment
GSV images and predict obstructions on sun glare. Based on the predicted
obstructions for given locations, we further estimated the time windows of sun
glare by estimating the sun positions and the relative angles between drivers
and the sun for those locations. We conducted a case study in Cambridge,
Massachusetts, USA. Results show that the method can predict the presence of
sun glare precisely. The proposed method would provide an important tool for
drivers and traffic planners to mitigate the sun glare and decrease the
potential traffic accidents caused by the sun glare.
