Lane Marking Detection and Reconstruction with Line-Scan Imaging Data

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.

Automatic Groove Measurement and Evaluation with High Resolution Laser Profiling Data

“…SVMs include linear and non-linear classification methods depending on kernel models or functions. Usually there are four types of kernel models, namely linear kernel, polynomial kernel, Gaussian kernel, and sigmoid kernel [ 25 , 26 , 27 , 28 , 29 , 30 ]. In this study the linear kernel and polynomial kernel are used to validate which method is more powerful in the separation of grooves and noises based on training data and test data.…”

Special Issue on Visual Sensors

“…Lane marking detection and localization are crucial for autonomous driving and lane-based pavement surveys. In [31], a novel methodology is presented for automated lane marking identification and reconstruction. In addition, a case study is given to validate the proposed methodology.…”

Detection method for auto guide vehicle’s walking deviation based on image thinning and Hough transform

“…The Hough transform converts the curve into point in the parameter space according to the duality of point and line, and detects the characteristics of the given curve by a voting algorithm. 23,24 A point on a straight line in the Euclidean space is a sinusoid in the parameter space. A plurality of points on the same straight line in the Euclidean space corresponds to sinusoidal clusters in the parameter space, and those curve clusters intersect at a point which is called the peak point.…”