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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MDPI AG
2021
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oai:doaj.org-article:661d21c87dc045c393be344b89f043312021-11-25T16:38:05ZROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings10.3390/app1122107832076-3417https://doaj.org/article/661d21c87dc045c393be344b89f043312021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10783https://doaj.org/toc/2076-3417One 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.Felipe FrancoMax Mauro Dias SantosRui Tadashi YoshinoLeopoldo Rideki YoshiokaJoão Francisco JustoMDPI AGarticleroad lane markinglane recognitioncomputer visionrandom sample consensus (RANSAC)statistical verificationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10783, p 10783 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
road lane marking lane recognition computer vision random sample consensus (RANSAC) statistical verification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
| spellingShingle |
road lane marking lane recognition computer vision random sample consensus (RANSAC) statistical verification Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Felipe Franco Max Mauro Dias Santos Rui Tadashi Yoshino Leopoldo Rideki Yoshioka João Francisco Justo ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings |
| description |
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. |
| format |
article |
| author |
Felipe Franco Max Mauro Dias Santos Rui Tadashi Yoshino Leopoldo Rideki Yoshioka João Francisco Justo |
| author_facet |
Felipe Franco Max Mauro Dias Santos Rui Tadashi Yoshino Leopoldo Rideki Yoshioka João Francisco Justo |
| author_sort |
Felipe Franco |
| title |
ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings |
| title_short |
ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings |
| title_full |
ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings |
| title_fullStr |
ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings |
| title_full_unstemmed |
ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings |
| title_sort |
roadlane—the modular framework to support recognition algorithms of road lane markings |
| publisher |
MDPI AG |
| publishDate |
2021 |
| url |
https://doaj.org/article/661d21c87dc045c393be344b89f04331 |
| work_keys_str_mv |
AT felipefranco roadlanethemodularframeworktosupportrecognitionalgorithmsofroadlanemarkings AT maxmaurodiassantos roadlanethemodularframeworktosupportrecognitionalgorithmsofroadlanemarkings AT ruitadashiyoshino roadlanethemodularframeworktosupportrecognitionalgorithmsofroadlanemarkings AT leopoldoridekiyoshioka roadlanethemodularframeworktosupportrecognitionalgorithmsofroadlanemarkings AT joaofranciscojusto roadlanethemodularframeworktosupportrecognitionalgorithmsofroadlanemarkings |
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1718413081318522880 |