Efficient and Robust Lane Detection Using Three-Stage Feature Extraction with Line Fitting

Lane detection plays an important role in advanced driver assistance system. The objective of lane detection is to separate lane markings from background and to locate their positions. Challenges of lane detection mainly lie in large appearance variations. These variations mainly include shadows, occlusion, variable lighting conditions, and different weather conditions, e.g., sunny or rainy days. In this paper, we propose a method named Lane Detection with Three-stage Feature Extraction to detect lane proposals. The three-stage features include contrast feature, line structure feature, and convex signal feature. Three feature extraction methods are devised for each of the three-stage respectively. After getting lane proposals through the processing of the three methods, we use straight line model to fit these proposals based on RANSAC method. Extensive experiments demonstrate that our method is stable and can find lane proposals exactly given the lane in the scene.