A novel method for predicting and mapping the presence of sun glare using Google Street View

TL;DR

This paper introduces a new approach using Google Street View images and neural networks to predict sun glare locations and times, aiming to reduce traffic accidents caused by sun glare.

Contribution

It presents a novel method combining GSV images and sun position estimation to predict sun glare, which is a new application in traffic safety analysis.

Findings

Accurately predicts sun glare presence in a case study.

Method effectively estimates sun glare time windows.

Potential to aid traffic safety planning.

Abstract

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.