Deep Learning based Traffic Direction Sign Detection and Determining Driving Style

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dc.authoridKaraduman, Mucahit/0000-0002-8087-4044
dc.authorwosidKaraduman, Mucahit/R-6853-2017
dc.authorwosidEren, Haluk/V-9711-2018
dc.contributor.authorKaraduman, Mucahit
dc.contributor.authorEren, Haluk
dc.date.accessioned2024-08-04T20:44:15Z
dc.date.available2024-08-04T20:44:15Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.description2017 International Conference on Computer Science and Engineering (UBMK) -- OCT 05-08, 2017 -- Antalya, TURKEYen_US
dc.description.abstractIntelligent automobiles and advanced driver assistance systems (ADAS) are some of the major technological developments that affect human daily life. Today, many studies are being generated to develop state of the art transportation systems. The general objective in these studies is to cope with negative effects of traffic. In this work, our aim is to contribute to the development of ADAS by determining driver behavior and traffic direction sign detection. The data employed are acquired by smartphone sensors, which are accelerometer, gyroscope, GPS, and camera, while the subject car moves between two specific points. The proposed method consists of two simultaneously running algorithms. The first one determines driver maneuvers, and the second one is the deep learning based algorithm that detects traffic direction sign using Convolution Neural Network (CNN). Here, the results of these two simultaneously running algorithms are assessed, and driving type is determined. GPS data is used for synchronization. Consequently, it is determined whether riding style is safe or aggressive, involving in traffic direction sign detection.en_US
dc.description.sponsorshipIEEE Adv Technol Human,Istanbul Teknik Univ,Gazi Univ,Atilim Univ,TBV,Akdeniz Univ,Tmmob Bilgisayar Muhendisleri Odasien_US
dc.identifier.endpage1050en_US
dc.identifier.isbn978-1-5386-0930-9
dc.identifier.scopus2-s2.0-85040555113en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1046en_US
dc.identifier.urihttps://hdl.handle.net/11616/98113
dc.identifier.wosWOS:000426856900197en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 International Conference on Computer Science and Engineering (Ubmk)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDriver behavioren_US
dc.subjectconvolution neural networken_US
dc.subjectdeep learningen_US
dc.subjecttraffic sign detectionen_US
dc.titleDeep Learning based Traffic Direction Sign Detection and Determining Driving Styleen_US
dc.typeConference Objecten_US

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