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dc.contributor.authorCáceres Hernández, Danilo
dc.contributor.authorFilonenko, Alexander
dc.contributor.authorSeo, Dongwook
dc.contributor.authorHyun Jo, Kang
dc.date.accessioned2018-06-29T21:43:15Z
dc.date.accessioned2018-06-29T21:43:15Z
dc.date.available2018-06-29T21:43:15Z
dc.date.available2018-06-29T21:43:15Z
dc.date.issued06/03/2015
dc.date.issued06/03/2015
dc.identifierhttps://ieeexplore.ieee.org/abstract/document/7281601/
dc.identifier.issn2163-5145
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5093
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5093
dc.descriptionTowards safe autonomous vehicle navigation the problem of lane detection and classification is highly important in the development of advanced driver assistance system (ADAS). This paper proposes a new method to detect the road lane marking for safe autonomous navigation purpose. It focuses on unconventional methods of identifying lane markings on a road surface through Laser Measurement System (LMS). This method was executed in three steps. Firstly, to detect lane markings a Density-based spatial clustering of applications with noise (DBSCAN) method was implemented. Secondly, in order to determine the surface course a distance clustering analysis was proposed. Thirdly, the Random Sample Consensus (RANSAC) line fitting method was implemented for removing the noise points around the road lane area. Lastly, an automatic peak detection was implemented to perform lane marking detection on road surfaces. Preliminary results were performed and tested on a group of consecutive fames to prove its effectiveness.en_US
dc.description.abstractTowards safe autonomous vehicle navigation the problem of lane detection and classification is highly important in the development of advanced driver assistance system (ADAS). This paper proposes a new method to detect the road lane marking for safe autonomous navigation purpose. It focuses on unconventional methods of identifying lane markings on a road surface through Laser Measurement System (LMS). This method was executed in three steps. Firstly, to detect lane markings a Density-based spatial clustering of applications with noise (DBSCAN) method was implemented. Secondly, in order to determine the surface course a distance clustering analysis was proposed. Thirdly, the Random Sample Consensus (RANSAC) line fitting method was implemented for removing the noise points around the road lane area. Lastly, an automatic peak detection was implemented to perform lane marking detection on road surfaces. Preliminary results were performed and tested on a group of consecutive fames to prove its effectiveness.en_US
dc.formatapplication/pdf
dc.formattext/html
dc.languageeng
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectLane markingen_US
dc.subjectrecognitionen_US
dc.subjectlaser scanningen_US
dc.subjectLane marking
dc.subjectrecognition
dc.subjectlaser scanning
dc.titleLane marking recognition based on laser scanningen_US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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