A Robust Lane Marking Extraction Algorithm for Self-Driving Vehicles

Vision-based lane detection for intelligent vehicles is a well-researched problem in the past decades. However, there are still many road conditions in which the lane marking extraction is very challenging. In this paper, a new lane marking extraction algorithm that performs better than the traditional Canny edge detector and Hough transform based techniques is proposed. The proposed system uses bird's eye view image with a 2D Gabor filter for lane marker enhancement followed by a marking extraction approach and a Bezier curve fitting technique. Preliminary test results show that the proposed algorithm works very well on highways and urban roads despite various environmental challenges such as shadows due to trees or bridges, road texture variations, and lighting conditions. In addition, the algorithm can run in real time at a rate of 25 frames per second for images of size 1280×720 pixels. Testing computer has Intel Core i7 processor with 8GB RAM and 3.6GHz frequency.