Abstract
This paper presents a method for lane boundaries detection which is not affected by the shadows, illumination and un-even road conditions. This method is based upon processing grayscale images using local gradient features, characteris-tic spectrum of lanes, and linear prediction. Firstly, points on the adjacent right and left lane are recognized using the local gradient descriptors. A simple linear prediction model is deployed to predict the direction of lane markers. The contribution of this paper is the use of vertical gradient image without converting into binary image(using suitable thre-shold), and introduction of characteristic lane gradient spectrum within the local window to locate the preciselane marking points along the horizontal scan line over the image. Experimental results show that this method has greater tolerance to shadows and low illumination conditions. A comparison is drawn between this method and recent methods reported in the literature.
Highlights
Lane detection has long been an important area of research for people in autonomous vehicle systems
157 image sequence was tested with our algorithm and it was found that 150 images were successfully detected with 7 false detections
An important advantage/contribution of this method is to track roads lane markers of various shape and locate precise lane marking points on each horizontal scan line which is not affected by presence of shadows and other low illumination condition
Summary
Lane detection has long been an important area of research for people in autonomous vehicle systems. The lane detection algorithm can be classified in two broad categories, namely model based [8,13,14,15,16,17,18] and feature based [1,7,11,12].The Model-based lane detection system begins with selecting a model/template for lane, extracting parameters to represent lanes, and fitting the model to the extracted parameters and processing Applications of these template based methods (linear, spline, parabola) are limited to some road environments and cannot perform well in the presence of shadows and low illumination condition. The use of Canny Hough Estimation of Vanishing Points (CHEVP) algorithm for locating the initial points is sensitive to choice of threshold This method is vulnerable to presence of shadows and illumination condition.
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