TY - GEN
T1 - Robust lane marking detection algorithm using drivable area segmentation and extended SLT
AU - Ozgunalp, Umar
AU - Fan, Rui
AU - Cheng, Shanshan
AU - Sun, Yuxiang
AU - Zuo, Weixun
AU - Zhu, Yilong
AU - Xue, Bohuan
AU - Zheng, Linwei
AU - Liang, Qing
AU - Liu, Ming
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85079047669&partnerID=8YFLogxK
U2 - 10.1109/ROBIO49542.2019.8961686
DO - 10.1109/ROBIO49542.2019.8961686
M3 - Conference article published in proceeding or book
AN - SCOPUS:85079047669
T3 - IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
SP - 2625
EP - 2629
BT - IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
PB - IEEE
T2 - 2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019
Y2 - 6 December 2019 through 8 December 2019
ER -