# Image-Based Dedicated Methods of Night Traffic Visibility Estimation
## 1 Introduction  
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]. On the highway, the reduction of visibility makes it difficult for the drivers to clearly see the road ahead of them, dramatically degrading driver's judgment in vehicles and causing various traffic accidents [2]. The visual conditions of nighttime driving are challenging for drivers, including dim street lighting, oncoming headlight glare, and the need for quick adaption across a wide range of lighting levels [3]. If visibility can be accurately identified and predicted in time, traffic accidents will be significantly reduced. At present, the attenuation characteristics of the laser in the atmosphere were generally used to estimate the visibility at major airports, highways, and meteorological monitoring stations. The implementation of this theory needs to be equipped with laser transmitting and receiving units. Although there are many hardware settings and fixed detection distance, this method is relatively mature, stable, and has less environmental interference. The visibility meter on the market mainly uses this method. In recent years, making use of image analysis to estimate visibility has attracted more and more attention. The advantages of these methods include fewer hardware facilities needed and no additional equipment required, and thus, the estimation can be made only with the video processed by the cameras. However, the shortcomings of these methods are also undeniable, such as low accuracy inspecial circumstances, high instability, and so on. [PERSON] et al. have developed a system which is used in-vehicle CCD cameras, aiming to estimate the visibility in adverse weather conditions, particularly in fog situations [4]. They presented several dedicated roadside sites and used them to validate the proposed model. [PERSON] et al. have identified an approach to estimate visibility conditions using an onboard camera and a digital map [5]. The method determines the current visual range in hazy conditions with reference to the characteristics of the traffic sign detectors in the fog and the registered detection by vision and information encoded in the map. [PERSON] et al. proposed a visibility range estimation algorithm for real-time adaptive speed-limit control in intelligent transportation systems [2]. Environmental data and road images are collected via roadside units. The analysis is performed using two image processing algorithms, namely, the improved dark channel prior (DCP) and weighted image entropy (WIE), and the support vector machine (SVM) classier is used to produce a visibility indicator in real-time. The method mainly relies on the road image taken by the camera to achieve real-time visibility estimation, and it also requires high precision and speed calculation of the analysis processing unit. According to the visual effects of night fog, [PERSON] et al. proposed two methods [6]. The first method evaluates the presence of fog around a vehicle due to the detection of the backscattered veil created by the headlamps. The second evaluates the presence of fog due to the detection of halos around light sources ahead of the vehicle. These two schemes can work in the condition either with streetlights or without streetlights. The scheme of recognizing the night fog by the halo of the lamp is a solution that can be widely applied to traffic. [PERSON] et al. discussed the possibility of application of a Convolutional Neural Network for visual atmospheric visibility estimation [7]. The authors implemented a Convolutional Neural Network with 3 convolution layers and trained it on a data set taken from CCTV cameras in South Korea. The authors divided the visibility condition into 20 levels, so the estimate results were not specific visibility values. [PERSON] et al.