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In this paper, a robust lane detection algorithm is proposed, where thevertical road profile of the road is estimated using dynamic programming fromthe v-disparity map and, based on the estimated profile, the road area issegmented. Since the lane markings are on the road area and any feature pointabove the ground will be a noise source for the lane detection, a mask iscreated for the road area to remove some of the noise for lane detection. Theestimated 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 ofsymmetrical local threshold (SLT), which not only considers dark light darktransition (DLD) of the lane markings, like (SLT), but also considersparallelism on the lane marking borders. The segmentation then uses only thefeature points that are on the road area. A maximum of two linear lane markingsare detected using an efficient 1D Hough transform. Then, the detected linearlane markings are used to create a region of interest (ROI) for parabolic lanedetection. Finally, based on the estimated region of interest, parabolic lanemodels are fitted using robust fitting. Due to the robust lane featureextraction and road area segmentation, the proposed algorithm robustly detectslane markings and achieves lane marking detection with an accuracy of 91% whentested on a sequence from the KITTI dataset.

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