Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

Lane Detection for Intelligent Vehicle under Different Illumination Conditions

Authors
Pingshu Ge, Lie Guo, Tao Zhang, Xiuchun Zhao, Jing Chang
Corresponding Author
Pingshu Ge
Available Online June 2017.
DOI
https://doi.org/10.2991/caai-17.2017.15How to use a DOI?
Keywords
intelligent vehicle; lane detection; linear lane model; Hough transform
Abstract
Lane detection is an important component for intelligent vehicle. A novel method for lane detection under complex illumination conditions is presented. An adaptive image segmentation technology based on OTSU algorithm was used to segment lane markings by combining the global and the local threshold. The improved Sobel operator was adapted to abstract the edges of the lane markings. The lane was finally identified using the improved Hough transform by limiting the range of lane parameters. On road experiments show that the proposed method has good accuracy and real-time performance under different illumination conditions.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Part of series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
978-94-6252-360-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/caai-17.2017.15How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Pingshu Ge
AU  - Lie Guo
AU  - Tao Zhang
AU  - Xiuchun Zhao
AU  - Jing Chang
PY  - 2017/06
DA  - 2017/06
TI  - Lane Detection for Intelligent Vehicle under Different Illumination Conditions
BT  - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 77
EP  - 80
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.15
DO  - https://doi.org/10.2991/caai-17.2017.15
ID  - Ge2017/06
ER  -