Abstract
Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. Numerous studies have been done to detect and locate lane markings with the purpose of advanced driver assistance systems, in which image data are usually captured by vision-based cameras. However, a limited number of studies have been done to identify lane markings using high-resolution laser images for road condition evaluation. In this study, the laser images are acquired with a digital highway data vehicle (DHDV). Subsequently, a novel methodology is presented for the automated lane marking identification and reconstruction, and is implemented in four phases: (1) binarization of the laser images with a new threshold method (multi-box segmentation based threshold method); (2) determination of candidate lane markings with closing operations and a marching square algorithm; (3) identification of true lane marking by eliminating false positives (FPs) using a linear support vector machine method; and (4) reconstruction of the damaged and dash lane marking segments to form a continuous lane marking based on the geometry features such as adjacent lane marking location and lane width. Finally, a case study is given to validate effects of the novel methodology. The findings indicate the new strategy is robust in image binarization and lane marking localization. This study would be beneficial in road lane-based pavement condition evaluation such as lane-based rutting measurement and crack classification.
Highlights
Road lane markings deteriorate from routine use, which can lead to unexpected traffic accidents for road users [1]
Images with sigmoid correction and a new threshold method; the second phase is to delineate all the laser images with sigmoid correction and a new threshold method; the second phase is to contour boxes or laneormarkings on closing operation andoperation marching algorithm; delineate all candidate contour boxes candidatebased lane markings based on closing andsquare marching the third phase is to separate out true lane marking from candidate lane marking using square algorithm; the third phase is to separate out true lane marking from candidate lane marking based on contour attributes; the last is to and reconstruct inconsecutive segments and using box based onand contour boxphase attributes; the last broken phase isand to reconstruct broken and inconsecutive and form thetraveling continuous lane marking along traveling
13, which shows that IMG1 has contour one contour indicating one candidate lane marking needs to be judged whether it belongs to true lane marking or not
Summary
Road lane markings deteriorate from routine use, which can lead to unexpected traffic accidents for road users [1]. Model-based methods use a few parameters or templates to represent the lines by assuming straight lines or parabolic curves [6,33] These techniques are more robust in noise removal, probably due to their high-level processing instead of pixel-based processing. To implement lane-based distress evaluation (i.e., pavement cracks, rutting measurement) using 2D laser images, a robust lane detection and localization approach is presented in this study. Results indicate the new methodology is robust and reliable in lane continuous measurement and evaluation of lane-based pavement distress for project- and network-level marking detection and localization for laser images. This study would be beneficial in continuous pavement survey. and evaluation of lane-based pavement distress for project- and network-level measurement pavement survey
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