Açık Akademik Arşiv Sistemi

Deep-Learning-Based Floor Path Model for Route Tracking of Autonomous Vehicles

Show simple item record

dc.contributor.authors Erginli, Mustafa; Cil, Ibrahim
dc.date.accessioned 2023-01-24T12:08:59Z
dc.date.available 2023-01-24T12:08:59Z
dc.date.issued 2022
dc.identifier.uri http://dx.doi.org/10.3390/systems10030083
dc.identifier.uri https://hdl.handle.net/20.500.12619/99737
dc.description Bu yayın 06.11.1981 tarihli ve 17506 sayılı Resmî Gazete’de yayımlanan 2547 sayılı Yükseköğretim Kanunu’nun 4/c, 12/c, 42/c ve 42/d maddelerine dayalı 12/12/2019 tarih, 543 sayılı ve 05 numaralı Üniversite Senato Kararı ile hazırlanan Sakarya Üniversitesi Açık Bilim ve Açık Akademik Arşiv Yönergesi gereğince telif haklarına uygun olan nüsha açık akademik arşiv sistemine açık erişim olarak yüklenmiştir.
dc.description.abstract Real-time route tracking is an important research topic for autonomous vehicles used in industrial facilities. Traditional methods such as copper line tracking on the ground, wireless guidance systems, and laser systems are still used in route tracking. In this study, a deep-learning-based floor path model for route tracking of autonomous vehicles is proposed. A deep-learning floor path model and algorithm have been developed for highly accurate route tracking, which avoids collisions of vehicles and follows the shortest route to reach the destination. The floor path model consists of markers. Routes in the floor path model are created by using these markers. The floor path model is transmitted to autonomous vehicles as a vector by a central server. The server dispatches the target marker address to the vehicle to move. The vehicle calculates all possible routes to this address and chooses the shortest one. Marker images on the selected route are processed using image processing and classified with a pre-trained deep-CNN model. If the classified image and the image on the selected route are the same, the vehicle proceeds toward its destination. While the vehicle moves on the route, it sends the last classified marker to the server. Other autonomous vehicles use this marker to determine the location of this vehicle. Other vehicles on the route wait to avoid a collision. As a result of the experimental studies we have carried out, the route tracking of the vehicles has been successfully achieved.
dc.language English
dc.language.iso eng
dc.publisher MDPI
dc.relation.isversionof 10.3390/systems10030083
dc.subject Social Sciences - Other Topics
dc.subject autonomous vehicles
dc.subject floor path model
dc.subject deep convolutional neural network
dc.subject route tracking
dc.subject transfer learning
dc.subject image processing
dc.title Deep-Learning-Based Floor Path Model for Route Tracking of Autonomous Vehicles
dc.type Article
dc.contributor.authorID Erginli, Mustafa/0000-0003-0931-6119
dc.contributor.authorID Cil, Ibrahim/0000-0002-1290-3704
dc.identifier.volume 10
dc.relation.journal SYSTEMS
dc.identifier.issue 3
dc.identifier.doi 10.3390/systems10030083
dc.identifier.eissn 2079-8954
dc.contributor.author Erginli, Mustafa
dc.contributor.author Cil, Ibrahim
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rights.openaccessdesignations gold


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record