期刊
SENSORS
卷 17, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/s17112475
关键词
road lane detection; shadows; fuzzy system; line segment detector
资金
- Basic Science Research Program through National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2015R1D1A1A01056761, NRF-2017R1D1A1B03028417]
- National Research Foundation of Korea (NRF) - Korea government (MSIP Ministry of Science, ICT & Future Planning) [NRF-2017R1C1B5074062]
- National Research Foundation of Korea [2015R1D1A1A01056761, 22A20152213086, 2017R1C1B5074062] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.
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