Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2840
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dc.contributor.authorÖzgül, Ozan Fırat-
dc.contributor.authorÇakır, Mehmet Ulaş-
dc.contributor.authorTan, Mehmet-
dc.contributor.authorAmasyalı, Mehmet Fatih-
dc.contributor.authorHayvacı, Harun Taha-
dc.date.accessioned2019-12-25T14:03:42Z-
dc.date.available2019-12-25T14:03:42Z-
dc.date.issued2018
dc.identifier.citationOzgul, O. F., Cakir, M. U., Tan, M., Amasyali, M. F., and Hayvaci, H. T. (2018, September). A Fully Unsupervised Framework for Scoring Driving Style. In 2018 International Conference on Intelligent Systems (IS) (pp. 228-234). IEEE.en_US
dc.identifier.isbn978-1-5386-7097-2
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2840-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8710574-
dc.description.abstractRating driving performance is a challenging topic. It attracts professionals from a variety of domains such as automotive industry and insurance companies. In this work, we propose a fully unsupervised driver scoring framework using a minimalistic dataset which is composed of Global Positioning System (GPS) and Controller Area Network (CAN Bus) data. Based on the natural expectation that good driving patterns should depend on the road type and traffic flow intensity, our framework attempts to assign a probabilistic score in proportion to the occurrence probability of a certain driving style given the road geometry and traffic conditions. Quantization of these random variables through clustering methods and learning of a co occurrence matrix between clusters of distinct variables provide a computationally relaxed way of otherwise intractable joint probability estimations. Utilizing this approach, we report explicitly different scoring results for aggressive and nonaggressive labelled driving experiences. Besides, we provide a rigorous analysis of clustering schemes applied on trajectory, traffic flow and driving style data.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDriving style scoringen_US
dc.subjectunsupervised learningen_US
dc.subjectmachine learningen_US
dc.titleA Fully Unsupervised Framework for Scoring Driving Styleen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage228
dc.identifier.endpage234
dc.authorid0000-0002-1741-0570-
dc.authorid0000-0002-6717-5484-
dc.identifier.wosWOS:000469337900033en_US
dc.identifier.scopus2-s2.0-85065994477en_US
dc.institutionauthorTan, Mehmet-
dc.institutionauthorHayvacı, Harun Taha-
dc.identifier.doi10.1109/IS.2018.8710574-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
crisitem.author.dept02.5. Department of Electrical and Electronics Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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