ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings

One of the main actions of the driver is to keep the vehicle in a road lane within its markings, which could be aided with modern driver-assistance systems. Forward digital cameras in vehicles allow deploying computer vision strategies to extract the road recognition characteristics in real-time to...

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Bibliographic Details
Published in:Applied sciences Vol. 11; no. 22; p. 10783
Main Authors: Franco, Felipe, Santos, Max Mauro Dias, Yoshino, Rui Tadashi, Yoshioka, Leopoldo Rideki, Justo, João Francisco
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2021
Subjects:
Algorithms
computer vision
Digital cameras
lane recognition
Noise
random sample consensus (RANSAC)
road lane marking
Signal processing
Statistical analysis
statistical verification
Traffic accidents & safety
Vehicles
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Summary:One of the main actions of the driver is to keep the vehicle in a road lane within its markings, which could be aided with modern driver-assistance systems. Forward digital cameras in vehicles allow deploying computer vision strategies to extract the road recognition characteristics in real-time to support several features, such as lane departure warning, lane-keeping assist, and traffic recognition signals. Therefore, the road lane marking needs to be recognized through computer vision strategies providing the functionalities to decide on the vehicle’s drivability. This investigation presents a modular architecture to support algorithms and strategies for lane recognition, with three principal layers defined as pre-processing, processing, and post-processing. The lane-marking recognition is performed through statistical methods, such as buffering and RANSAC (RANdom SAmple Consensus), which selects only objects of interest to detect and recognize the lane markings. This methodology could be extended and deployed to detect and recognize any other road objects.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app112210783