A Lane Detection Method Combined Fuzzy Control with RANSAC Algorithm

The traditional lane detection methods based on the RANSAC algorithm used to cause many false detection and unable to accurately detect the lanes in complex road environment, because of the existence of interferential noise points in the set of sampling points. Aiming at these issues, this paper presents a new lane detection method combined fuzzy control with RANSAC algorithm. The first process of the new lane detection method is pretreatment, the purpose of which is to denoise the image preliminarily through filtering and binarization. And then it selects the region of interest (ROI) that contains lanes in the input image and extract the initial boundary candidate points of the lanes in ROI. So far, there are still a lot of irrelevant noise points in the set of lane boundary candidate points. It would analyze the relationship between the interferential noise points and the boundary points of the lane, and then remove the interferential noise points from the set of lane boundary candidate point by using the fuzzy control. After that, fit the lane model by using RANSAC algorithm in the set of effective lane boundary points. The experiment shows that the method proposed in this paper has high robustness and effectiveness which can accurately detect the lanes in complicated city road.