Image-Based Dedicated Methods of Night Traffic Visibility Estimation

Abstract

Traffic visibility is an essential reference for safe driving. Nighttime conditions add to the difficulty of estimating traffic visibility. To estimate the visibility in nighttime traffic images, we propose a Traffic Sensibility Visibility Estimation (TSVE) algorithm that combines laser transmission and image processing and needs no reference to the corresponding fog-free images and camera calibration. The information required is first obtained via the roadside equipment which collects environmental data and captures road images and then analyzed locally or remotely. The proposed analysis includes calculating the current atmospheric transmissivity with the laser atmospheric transmission theory and acquiring image features by using the cameras and the adjustable brightness target. Image analysis is performed using two image processing algorithms, namely, dark channel prior (DCP) and image brightness contrast. Finally, to improve the accuracy of visibility estimation, multiple nonlinear regression (MNLR) is performed on the various visibility indicators obtained by the two methods. Extensive on-site measurements analysis confirms the advantages of TSVE. Compared with other visibility estimation methods, such as the laser atmospheric transmission theory and image analysis method, TSVE significantly decreases the estimation errors.