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모델 통합 기반 상대 차량 주행 경로 예측 알고리즘

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dc.contributor.authorCho, Jeongmin-
dc.contributor.authorHuh, Kunsoo-
dc.date.accessioned2021-07-30T05:22:46Z-
dc.date.available2021-07-30T05:22:46Z-
dc.date.issued2020-01-
dc.identifier.issn1225-6382-
dc.identifier.issn2234-0149-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4461-
dc.description.abstractIn this paper, the target vehicle trajectory prediction is proposed in order to improve the accuracy and robustness against the noise of the sensors. The physics-based model and maneuver-based model are composed and integrated to predict the trajectory accurately. First, the physics-based model is designed using the relative position information of the vehicle. Second, the maneuver-based model reflects the vehicle driving pattern, such as lane keeping or changing. The maneuver-based predicted model candidates are selected through a probabilistic approach. The integrated prediction algorithm is implemented through a physics-based model and a stochastically selected trajectory candidate. In particular, the proposed algorithm can only be designed by using the onboard sensor data, and it is validated by using the computer simulation software, CarSim and MATLAB/Simulink, and experimental test.-
dc.format.extent8-
dc.language한국어-
dc.language.isoKOR-
dc.publisher한국자동차공학회-
dc.title모델 통합 기반 상대 차량 주행 경로 예측 알고리즘-
dc.title.alternativeTarget vehicle trajectory prediction algorithm based on model integration-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.7467/KSAE.2020.28.1.001-
dc.identifier.scopusid2-s2.0-85080047544-
dc.identifier.bibliographicCitation한국자동차공학회 논문집, v.28, no.1, pp 1 - 8-
dc.citation.title한국자동차공학회 논문집-
dc.citation.volume28-
dc.citation.number1-
dc.citation.startPage1-
dc.citation.endPage8-
dc.type.docTypeArticle-
dc.identifier.kciidART002537878-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorTrajectory prediction-
dc.subject.keywordAuthorPhysics based model-
dc.subject.keywordAuthorManeuver based model-
dc.subject.keywordAuthorMaximum likelihood-
dc.subject.keywordAuthorModel integration-
dc.identifier.urlhttp://journal.ksae.org/_common/do.php?a=full&bidx=1806&aidx=22269-
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서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles

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