Xin Yang
ABSTRACT
Aiming at the problem of large error in detecting lane lines on curved, long distance and complex roads by autonomous vehicles, a lane line detection algorithm based on dynamic ROI (Region Of Interest) and adaptive thresholding segmentation is proposed. According to the principle of inverse perspective transform, the original image is converted into an aerial view, and then the aerial view is preprocessed to calculate the gray column mean and full mean to dynamically determine the ROI. The image array is expanded for differentiation calculation. The adaptive threshold segmentation is carried out according to the characteristics of partition. The area and transverse and longitudinal span of each connected area in the binary diagram are calculated, and the interference factors are filtered out as required. Using the curve fitting algorithm of RANSAC (RANdom SAmple Consensus), the quadratic polynomial is defined as the target curve model to fit the characteristic points of lane line. The results show that the algorithm can accurately detect the lane lines under various road conditions.
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