Detailed Information

Cited 0 time in webofscience Cited 3 time in scopus
Metadata Downloads

An Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle

Full metadata record
DC Field Value Language
dc.contributor.authorShin, Donghoon-
dc.contributor.authorYi, Subin-
dc.contributor.authorPark, Kang-moon-
dc.contributor.authorPark, Manbok-
dc.date.available2021-02-22T05:35:35Z-
dc.date.issued2020-03-
dc.identifier.issn2076-3417-
dc.identifier.urihttps://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/2498-
dc.description.abstractFeatured Application Motion Prediction, Target Intent Inference, Urban Intersection, Automated Driving. Abstract Research shows that urban intersections are a hotspot for traffic accidents which cause major human injuries. Predicting turning, passing, and stop maneuvers against surrounding vehicles is considered to be fundamental for advanced driver assistance systems (ADAS), or automated driving systems in urban intersections. In order to estimate the target intent in such situations, an interacting multiple model (IMM)-based intersection-target-intent estimation algorithm is proposed. A driver model is developed to represent the driver's maneuvering on the intersection using an IMM-based target intent classification algorithm. The performance of the intersection-target-intent estimation algorithm is examined through simulation studies. It is demonstrated that the intention of a target vehicle is successfully predicted based on observations at an individual intersection by proposed algorithms.-
dc.language영어-
dc.language.isoENG-
dc.publisherMDPI-
dc.titleAn Interacting Multiple Model Approach for Target Intent Estimation at Urban Intersection for Application to Automated Driving Vehicle-
dc.typeArticle-
dc.publisher.location스위스-
dc.identifier.doi10.3390/app10062138-
dc.identifier.scopusid2-s2.0-85082652557-
dc.identifier.wosid000529252800239-
dc.identifier.bibliographicCitationAPPLIED SCIENCES-BASEL, v.10, no.6-
dc.citation.titleAPPLIED SCIENCES-BASEL-
dc.citation.volume10-
dc.citation.number6-
dc.type.docTypeLetter-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaChemistry-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaMaterials Science-
dc.relation.journalResearchAreaPhysics-
dc.relation.journalWebOfScienceCategoryChemistry, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryEngineering, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryMaterials Science, Multidisciplinary-
dc.relation.journalWebOfScienceCategoryPhysics, Applied-
dc.subject.keywordPlusALGORITHMS-
dc.subject.keywordPlusTRACKING-
dc.subject.keywordAuthormotion prediction-
dc.subject.keywordAuthortarget intent inference-
dc.subject.keywordAuthorurban intersection-
dc.subject.keywordAuthorautomated driving-
dc.identifier.urlhttps://www.mdpi.com/2076-3417/10/6/2138-
Files in This Item
Go to Link
Appears in
Collections
공과대학 > 기계시스템학부 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE