Abstract
Lane detection plays an important role in improving autopilot’s safety. In this paper, a novel lane-division-lines detection method is proposed, which exhibits good performances in abnormal illumination and lane occlusion. It includes three major components: First, the captured image is converted to aerial view to make full use of parallel lanes’ characteristics. Second, a ridge detector is proposed to extract each lane’s feature points and remove noise points with an adaptable neural network (ANN). Last, the lane-division-lines are accurately fitted by an improved random sample consensus (RANSAC), termed the (regional) gaussian distribution random sample consensus (G-RANSAC). To test the performances of this novel lane detection method, we proposed a new index named the lane departure index (LDI) describing the departure degree between true lane and predicted lane. Experimental results verified the superior performances of the proposed method over others in different testing scenarios, respectively achieving 99.02%, 96.92%, 96.65% and 91.61% true-positive rates (TPR); and 66.16, 54.85, 55.98 and 52.61 LDIs in four different types of testing scenarios.
Highlights
Lane-division line detection plays a critical role in improving the safety level of intelligent electric vehicles (IEVs)
The proposed method achieved 99.02%, 96.92%, 96.65%, and 91.61% true-positive rates (TPR) in the four different the whole ridges’
We proposed a lane-division-lines detection method based on ridge detector and regional G-random sample consensus (RANSAC)
Summary
Lane-division line detection plays a critical role in improving the safety level of intelligent electric vehicles (IEVs). There are two main methods to detect lane-division-lines: feature-based detection and model-based detection. Peng proposed a lane-division line detection method with the statistical Hough transform based on a gradient constraint [7]. Ma converted the color space of RGB to the CIELab color model and detected the lane-division-lines by using k-means clustering [8]. Jung proposed a lane-division line detection method based on the Haar feature [10]. Other feature-based detection methods [12,13,14,15,16,17] achieved good performances in normal-conditions scenarios; the main drawback of this approach is that it is disturbed by noise, as it ignores the model of the lane-division-lines
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