Robust lane detection & tracking based on novel feature extraction and lane categorization

In this paper, we introduce a robust lane detection and tracking algorithm to cope with complex scenarios and to decrease the effect of thresholds. For lane feature extraction, an extension to the symmetrical local threshold (SLT) is proposed to improve the feature map and obtain orientation information. Then, while creating a Hough accumulator, obtained orientation information is used to decrease computational complexity (≈ 60 times) and acquire a clearer accumulator. The left and right lanes are categorized by applying a mask on the Hough accumulator, which leads to low computational complexity and reduced sensitivity to thresholding. To quantify the new feature map, we used ground truth lane markings from the RoMa Datasets and the optimum true positive (TP) to positive (P) ratio increased from 69% to 86% on average, compared to the SLT. The successful lane detection rate calculated from more than 10K frames is, 96.2%, demonstrating the robustness of the system.