Detailed Information

Cited 0 time in webofscience Cited 6 time in scopus
Metadata Downloads

Intervention minimized semi-Autonomous control using decoupled model predictive control

Full metadata record
DC Field Value Language
dc.contributor.authorKim, Hayoung-
dc.contributor.authorCho, Jeongmin-
dc.contributor.authorKim, Dongchan-
dc.contributor.authorHuh, Kunsoo-
dc.date.accessioned2021-07-30T05:24:52Z-
dc.date.available2021-07-30T05:24:52Z-
dc.date.created2021-05-13-
dc.date.issued2017-07-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/4773-
dc.description.abstractThis paper proposes semi-Autonomous control that minimizes intervention considering drivers steering and braking intentions. The biggest challenge of this problem is how to fairly judge drivers intentions that appear differently in the lateral and longitudinal directions and how to minimize controller intervention. A decoupled model predictive control (MPC) and optimal intervention decision methods are proposed considering driver incompatibility. Several MPCs are designed first considering the fact that the driver can avoid obstacles either by braking or moving to the left or right lanes. The control input to avoid the collision is calculated for each MPC such that its intervention can be minimized reflecting the drivers intention. After driver incompatibility is formalized, the optimal input is selected to minimize the incompatibility among the paths that can avoid accidents. The proposed algorithm is validated in simulations where collision can be avoided while minimizing the intervention.-
dc.language영어-
dc.language.isoen-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleIntervention minimized semi-Autonomous control using decoupled model predictive control-
dc.typeArticle-
dc.contributor.affiliatedAuthorHuh, Kunsoo-
dc.identifier.doi10.1109/IVS.2017.7995787-
dc.identifier.scopusid2-s2.0-85028041494-
dc.identifier.bibliographicCitation2017 IEEE Intelligent Vehicles Symposium (IV), pp.618 - 623-
dc.relation.isPartOf2017 IEEE Intelligent Vehicles Symposium (IV)-
dc.citation.title2017 IEEE Intelligent Vehicles Symposium (IV)-
dc.citation.startPage618-
dc.citation.endPage623-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusIntelligent vehicle highway systems-
dc.subject.keywordPlusModel predictive control-
dc.subject.keywordPlusPredictive control systems-
dc.subject.keywordPlusAvoid obstacles-
dc.subject.keywordPlusControl inputs-
dc.subject.keywordPlusDecision method-
dc.subject.keywordPlusDecoupled model-
dc.subject.keywordPlusLongitudinal direction-
dc.subject.keywordPlusOptimal intervention-
dc.subject.keywordPlusSemi-autonomous control-
dc.subject.keywordPlusBraking-
dc.identifier.urlhttps://ieeexplore.ieee.org/document/7995787-
Files in This Item
Go to Link
Appears in
Collections
서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles

qrcode

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

Related Researcher

Researcher Huh, Kunsoo photo

Huh, Kunsoo
COLLEGE OF ENGINEERING (DEPARTMENT OF AUTOMOTIVE ENGINEERING)
Read more

Altmetrics

Total Views & Downloads

BROWSE