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Keywords = lane marking detector

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29 pages, 13934 KiB  
Article
Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor
by Toan Minh Hoang, Na Rae Baek, Se Woon Cho, Ki Wan Kim and Kang Ryoung Park
Sensors 2017, 17(11), 2475; https://doi.org/10.3390/s17112475 - 28 Oct 2017
Cited by 43 | Viewed by 9891
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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33 pages, 3165 KiB  
Article
A Low Cost Sensors Approach for Accurate Vehicle Localization and Autonomous Driving Application
by Rafael Vivacqua, Raquel Vassallo and Felipe Martins
Sensors 2017, 17(10), 2359; https://doi.org/10.3390/s17102359 - 16 Oct 2017
Cited by 51 | Viewed by 12014
Abstract
Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where [...] Read more.
Autonomous driving in public roads requires precise localization within the range of few centimeters. Even the best current precise localization system based on the Global Navigation Satellite System (GNSS) can not always reach this level of precision, especially in an urban environment, where the signal is disturbed by surrounding buildings and artifacts. Laser range finder and stereo vision have been successfully used for obstacle detection, mapping and localization to solve the autonomous driving problem. Unfortunately, Light Detection and Ranging (LIDARs) are very expensive sensors and stereo vision requires powerful dedicated hardware to process the cameras information. In this context, this article presents a low-cost architecture of sensors and data fusion algorithm capable of autonomous driving in narrow two-way roads. Our approach exploits a combination of a short-range visual lane marking detector and a dead reckoning system to build a long and precise perception of the lane markings in the vehicle’s backwards. This information is used to localize the vehicle in a map, that also contains the reference trajectory for autonomous driving. Experimental results show the successful application of the proposed system on a real autonomous driving situation. Full article
(This article belongs to the Special Issue Mechatronic Systems for Automatic Vehicles)
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