High Speed Lane Recognition under Complex Road Conditions

To improve the speed and stability of lanes recognition under complex road conditions, a rapid detection algorithm combined with dynamic window and prior knowledge is proposed. The method of grid is used to segment initial image and define the region of interest (ROI), then all the pixels are eliminated except those on the intersection of the grid lines, thus feature pixels of the lane edge in these intersections will be detected and can be used to generate some dynamic windows with the dilatation algorithm. Then, images are processed in these dynamic windows to obtain lane edge feature. Slope and intercept of the lane can be attained by Hough transformation. To obtain the correct lane information, the information obtained in current processing cycle will be judged and filtered by the prior knowledge. Experiments in structured road showed that the speed of image processing was about 22ms/frame and the proposed algorithm could meet the real-time and stability requirements of high-speed vehicle vision navigation system.