DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, Chanyoung | ko |
dc.contributor.author | Seong, HK | ko |
dc.contributor.author | Shim, David Hyunchul | ko |
dc.date.accessioned | 2020-10-23T01:56:21Z | - |
dc.date.available | 2020-10-23T01:56:21Z | - |
dc.date.created | 2020-07-14 | - |
dc.date.created | 2020-07-14 | - |
dc.date.issued | 2020-05 | - |
dc.identifier.citation | Journal of Institute of Control, Robotics and Systems, v.26, no.5, pp.342 - 347 | - |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.uri | http://hdl.handle.net/10203/276934 | - |
dc.description.abstract | .In recent years, autonomous vehicles have been developed by various approaches for traffic safety and driver convenience. End-to-end learning-based autonomous driving has gained enormous attention in conjunction with deep learning technologies in which perception, planning, and control of the conventional autonomous driving algorithm are trained by a single deep neural network. In this paper, we present the end-to-end learning-based autonomous driving framework. The framework consisted of three parts: data acquisition in real-world and simulated environments, network design and optimization, and performance evaluation. Our framework was integrated on a full-scale autonomous vehicle platform and evaluated with various performance metrics. | - |
dc.language | Korean | - |
dc.publisher | Institute of Control, Robotics and Systems | - |
dc.title | Development of the end- to-end learning based autonomous driving framework and experiments on a full-scale autonomous vehicle | - |
dc.title.alternative | End to End 학습 기반 자율 주행 프레임워크 개발 및 실차 기반 실험 | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85086273150 | - |
dc.type.rims | ART | - |
dc.citation.volume | 26 | - |
dc.citation.issue | 5 | - |
dc.citation.beginningpage | 342 | - |
dc.citation.endingpage | 347 | - |
dc.citation.publicationname | Journal of Institute of Control, Robotics and Systems | - |
dc.identifier.doi | 10.5302/J.ICROS.2020.20.0012 | - |
dc.identifier.kciid | ART002585050 | - |
dc.contributor.localauthor | Shim, David Hyunchul | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordPlus | end to end deep learning | - |
dc.subject.keywordPlus | autonomous driving | - |
dc.subject.keywordPlus | experiment | - |
dc.subject.keywordPlus | framework | - |
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