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.
Highlights
In bad weather, the absorption or scattering of light by atmospheric particles such as rain, snow, fog, or haze can significantly reduce the visibility of scenes [1]
By extracting the visibility characteristics obtained from laser transmission and image processing, we can estimate the visibility of foggy traffic scenes at night without reference to the corresponding fog-free images and camera calibration
We have presented a traffic perception visibility estimation algorithm that combines laser transmission and image processing
Summary
The absorption or scattering of light by atmospheric particles such as rain, snow, fog, or haze can significantly reduce the visibility of scenes [1]. The method mainly relies on the road image taken by the camera to achieve real-time visibility estimation, and it requires high precision and speed calculation of the analysis processing unit. Some people have proposed methods for estimating visibility, which combine laser transmission and image analysis. Experiments using visual acuity charts show a link between fog concentration and vision These methods are not suitable for road visibility estimation, because the use of images is limited to the simple analysis of laser image contrast and eye chart. We propose a Traffic Sensibility Visibility Estimation (TSVE) algorithm which combines laser transmission and image analysis. By extracting the visibility characteristics obtained from laser transmission and image processing, we can estimate the visibility of foggy traffic scenes at night without reference to the corresponding fog-free images and camera calibration. Once the visibility is estimated, the driver can adjust his or her current driving speed
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