Deep Learning based Vehicle-To-Vehicle See Through System with Auto-Start Function

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dc.contributor.authorKaya, Mehmet Mucahit
dc.contributor.authorOzisik, Maide Elif
dc.contributor.authorBaykar, Ali Omer
dc.contributor.authorYannier, Selim
dc.contributor.authorYildiz, Tuba Conka
dc.date.accessioned2025-02-20T08:46:30Z
dc.date.available2025-02-20T08:46:30Z
dc.date.issued2022
dc.departmentTürk-Alman Üniversitesien_US
dc.description6th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2022 -- 20 October 2022 through 22 October 2022 -- Ankara -- 184355en_US
dc.description.abstractToday, with the increasing human population, the need for transportation is also increasing. These increasing transportation needs bring along some problems such as traffic density and traffic accidents. It is known that many of these accidents are caused by incorrect overtaking on single-lane roads and highways. In this research study, a solution proposal for such wrongful overtaking situations is discussed. The most important of these situations is that the vehicle to be overtaken is obstructing the view. In this study, by establishing a WLAN-based communication system between vehicles, it is discussed to eliminate the related loss of vision by image transfer between each vehicles. Unlike existing systems, this image transfer process is equipped with an automatic trigger with artificial intelligence. When overtaking is required, it is determined by means of deep networks whether vision loss occurs or not. As a main essence of this work, If vision loss is detected, the image transfer process between vehicles starts automatically and informs the driver. As a result of the real-Time tests, it has been observed that it works successfully within the processing time required for the system to process 20 fps image data at a distance of 100 meters. © 2022 IEEE.
dc.description.sponsorshipSiemens Kartal R&D Center; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (1139B412100719)
dc.identifier.doi10.1109/ISMSIT56059.2022.9932864
dc.identifier.endpage590en_US
dc.identifier.isbn978-166547013-1
dc.identifier.scopus2-s2.0-85142858078
dc.identifier.startpage587en_US
dc.identifier.urihttps://doi.org/10.1109/ISMSIT56059.2022.9932864
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1757
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofISMSIT 2022 - 6th International Symposium on Multidisciplinary Studies and Innovative Technologies, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250220
dc.subjectDeep Learningen_US
dc.subjectLane Changeen_US
dc.subjectObject Detectionen_US
dc.subjectSee Through Systemen_US
dc.subjectVehicle to Vehicle Communicationen_US
dc.titleDeep Learning based Vehicle-To-Vehicle See Through System with Auto-Start Function
dc.typeConference Object

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