Robust lane-detection algorithm based on improved symmetrical local threshold for feature extraction and inverse perspective mapping

Abstract

Here, an extended version of the symmetrical local threshold (SLT) algorithm is introduced for lane feature extraction and used in a novel lane-detection system. The introduced feature map extractor utilises parallel lane border features as well as the dark-light-dark (DLD) pattern of the lane marking used in SLT. Hence, compared to the SLT, the true positive to positive rate of the calculated feature maps is increased from 69% to 86% on the ROMA dataset. In addition, the proposed algorithm supplies orientation information for the estimated feature points, which can be useful for many optimisation algorithms. Consequently, based on the estimated lane feature orientations, a global lane orientation is calculated and used for both enhancing the feature map and estimating a one-dimensional (1D) lateral offset likelihood function. Then, the estimated 1D functions are filtered temporally and up to two linear lane candidates are detected. For increased flexibility, robust fitting is applied to the feature points in the region of interest (ROI). Finally, based on the detection of the previous frame, a mask is created and applied to the next frame. When tested on 2301 road images, mean error in lateral offset is calculated as 4.1 pixel on the IPM images.