Robust Lane Marking Detection Algorithm Using Drivable Area Segmentation and Extended SLT

TL;DR

This paper presents a robust lane detection algorithm that combines road area segmentation, an extended symmetrical local threshold method, and linear and parabolic lane modeling to improve accuracy in complex driving scenarios.

Contribution

The novel integration of road segmentation with an extended SLT method and robust lane modeling enhances lane detection accuracy and robustness over previous approaches.

Findings

Achieved 91% detection accuracy on KITTI dataset

Effectively filters noise using road area masks

Successfully detects up to two lane markings simultaneously

Abstract

In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset.