# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 1 Introduction
proposed a CNN-RNN model for directly estimating relative atmospheric visibility from outdoor photos without relying on weather images or data that require expensive sensing or custom capture [8]. The CNN captures the global view while the RNN simulates human's attention shift, namely, from the whole image (global) to the farthest discerned region (local). Present literature shows that most methods for road visibility estimation are performed on two separate methods, namely, laser transmission and image processing. The measurement range of the laser atmospheric transmission method is not wide enough. The image processing method is affected strongly by camera quality. Based on this, some people have proposed methods for estimating visibility, which combine laser transmission and image analysis. [PERSON] et al. built a system that uses light-scattering properties to estimate the visibility distance in foggy conditions [9]. The laser emitter is mounted next to the camera. The camera captures the laser images during the launch and estimates the visibility distance by image characteristics such as the contrast. [PERSON] et al. have presented some principles which can lead to the development of a system capable of estimating the visibility distance in foggy weather conditions by using the light scattering property [10]. The effect of fog concentration on the light source and the relationship between fog concentration and visual acuity are used as the input data. The authors analyzed the influence of the LED light source and the laser light source on the fog environment. 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. Consequently, 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. Guided by laser atmospheric transmission theory, TSVE organizes laser emitters and receivers to detect the transmissivity of the current atmospheric environment. For the road images and the adjustable brightness target images, the dark channel prior (DCP) algorithm is used to detect and calculate the image transmittance [11]; then, the gray distribution information in the brightness and contrast of the adjustable brightness target images is extracted for visibility calculation. The adjustable brightness target can be applied to different brightness environments. Multiple nonlinear regression (MNLR) is used to fuse the multiple different visibility features obtained before. Due to the low ambient brightness at night, the final estimation results may be different from human perception. Based on the actual nighttime ambient brightness, we adjust the nighttime road visibility. The algorithm has been evaluated by using actual traffic images and reference visibility meter. TSVE has significant advantages compared with the method of laser atmospheric transmission theory or image analysis. Once the visibility is estimated, the driver can adjust his or her current driving speed accordingly.