Visibility detection and prediction of foggy highway based on lane line detection and Winters additive model

Highway fog is one of the main reasons of causing a traffic accident, so the fog highway visibility detection is very important. In the existing methods, it is necessary to set an appropriate target in advance for visibility calculation and most of the methods are based on neural network for visibility prediction, which restricts the efficiency of visibility detection and prediction. In view of the above problems, this paper proposes a highway visibility detection and prediction method based on lane detection and Winters additive model in foggy days. This method is based on the dark channel prior theory to obtain the highway image after fog removal and extract the coordinates of the end points of the lane line from the image. The visibility of highway in foggy days is calculated with the lane line as the reference. The visibility time series were fitted and predicted based on Winters additive model. The experimental results show that the method can accurately calculate and predict the highway visibility in foggy days without the need of camera calibration.