NDLI: Robust lane detection based on gradient direction

Content Provider	IEEE Xplore Digital Library
Author	Yong Chen Mingyi He Yifan Zhang
Copyright Year	2011
Description	Author affiliation: Department of Electronic and Information Engineering, Shaanxi Provincial Key Laboratory of Information Acquisition and Processing (IAP), School of Electronics and Information, Northwestern Polytechnical University, Xi'an, 710129, China (Yong Chen; Mingyi He; Yifan Zhang)
Abstract	A robust and effective method to detect lane in the images captured with a vehicle-mounted monocular camera in challenging environments is proposed in this paper. In the newly proposed approach, the gradient direction (GD) feature and the lane boundaries projection model are used. Using GD feature and GD Gaussian distribution with the likelihood function, the lane detection is performed by employing maximum a posteriori (MAP) estimation with prior knowledge. Afterwards, the model parameter values are estimated, with which the lane geometric structure (such as the lane curvature and change rate), the host vehicle position and heading direction in the lane can be also calculated. The experimental results show that the method works more robustly and accurately in various situations with the broken and worn lane markings, the curved lane, the messy shadows, the sun glare, the occlusion of other vehicles, the dusky light in the evening, etc.
Starting Page	1547
Ending Page	1552
File Size	889475
Page Count	6
File Format	PDF
ISBN	9781424487547
e-ISBN	9781424487561
e-ISBN	9781424487554
DOI	10.1109/ICIEA.2011.5975836
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Roads Pixel Vehicles Cameras Robustness Feature extraction Sun maximum a posteriori estimation lane detection gradient direction lane projection model
Content Type	Text
Resource Type	Article

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