A Robust Method for Detecting Lane Boundary in Challenging Scenes

Lane boundary detection plays a key role in the driver assistance system. This study proposed a robust method for detecting lane boundary in challenging scenes. First, a horizontal line is detected from the original image using improved Vertical Mean Distribution Method (iVMD) and the sub-region image which is under the horizontal line, is determined. Second, we extracted the lane marking from the sub-region image using Canny edge detector. Finally, K-means clustering algorithm classified left and right lane cluster before using RANSAC algorithm which fits a line model to each cluster. The proposed algorithm demonstrates the accuracy with respect to variant illumination, cracked road, complex lane marking and passing traffic. Experimental results show that the proposed method satisfies the real-time and efficient requirement of the intelligent transportation systems.