Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 배인환 | ko |
dc.contributor.author | 김영후 | ko |
dc.contributor.author | 김태경 | ko |
dc.contributor.author | 오민호 | ko |
dc.contributor.author | 주현수 | ko |
dc.contributor.author | 김슬기 | ko |
dc.contributor.author | 신관준 | ko |
dc.contributor.author | 윤선재 | ko |
dc.contributor.author | 이채진 | ko |
dc.contributor.author | 임용섭 | ko |
dc.contributor.author | 최경호 | ko |
dc.date.accessioned | 2019-07-04T07:49:11Z | - |
dc.date.available | 2019-07-04T07:49:11Z | - |
dc.date.created | 2019-07-04 | - |
dc.date.issued | 2019-06 | - |
dc.identifier.citation | 자동차안전학회지, v.11, no.2, pp.35 - 43 | - |
dc.identifier.issn | 2005-9396 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/10102 | - |
dc.description.abstract | This paper describes the improved environment recognition algorithms using some type of sensors like LiDAR and cameras. Additionally, integrated control algorithm for an autonomous vehicle is included. The integrated algorithm was based on C++ environment and supported the stability of the whole driving control algorithms. As to the improved vision algorithms, lane tracing and traffic sign recognition were mainly operated with three cameras. There are two algorithms developed for lane tracing, Improved Lane Tracing (ILT) and Histogram Extension (HIX). Two independent algorithms were combined into one algorithm – Enhanced Lane Tracing with Histogram Extension (ELIX). As for the enhanced traffic sign recognition algorithm, integrated Mutual Validation Procedure (MVP) by using three algorithms - Cascade, Reinforced DSIFT SVM and YOLO was developed. Comparing to the results for those, it is convincing that the precision of traffic sign recognition is substantially increased. With the LiDAR sensor, static and dynamic obstacle detection and obstacle avoidance algorithms were focused. Therefore, improved environment recognition algorithms, which are higher accuracy and faster processing speed than ones of the previous algorithms, were proposed. Moreover, by optimizing with integrated control algorithm, the memory issue of irregular system shutdown was prevented. Therefore, the maneuvering stability of the autonomous vehicle in severe environment were enhanced. | - |
dc.language | Korean | - |
dc.publisher | 사단법인 한국자동차안전학회 | - |
dc.title | 자율주행 제어를 위한 향상된 주변환경 인식 알고리즘 | - |
dc.title.alternative | Improved Environment Recognition Algorithms for Autonomous Vehicle Control | - |
dc.type | Article | - |
dc.identifier.doi | 10.22680/kasa2019.11.2.035 | - |
dc.type.local | Article(Domestic) | - |
dc.type.rims | ART | - |
dc.description.journalClass | 2 | - |
dc.identifier.kciid | ART002479565 | - |
dc.identifier.citationVolume | 11 | - |
dc.identifier.citationNumber | 2 | - |
dc.identifier.citationStartPage | 35 | - |
dc.identifier.citationEndPage | 43 | - |
dc.identifier.citationTitle | 자동차안전학회지 | - |
dc.description.isOpenAccess | N | - |
dc.subject.keywordAuthor | Obstacle detection and avoidance | - |
dc.subject.keywordAuthor | 물체 인식 및 회피 | - |
dc.subject.keywordAuthor | Recognition algorithm | - |
dc.subject.keywordAuthor | 인식 알고리즘 | - |
dc.subject.keywordAuthor | Sign detection | - |
dc.subject.keywordAuthor | 표지판 인식 | - |
dc.subject.keywordAuthor | Autonomous vehicle | - |
dc.subject.keywordAuthor | 자율주행차 | - |
dc.subject.keywordAuthor | Cross-checking system | - |
dc.subject.keywordAuthor | 상호 확인 시스템 | - |
dc.subject.keywordAuthor | Image machine learning | - |
dc.subject.keywordAuthor | 이미지 기계 학습 | - |
dc.subject.keywordAuthor | Integrated control algorithm | - |
dc.subject.keywordAuthor | 통합제어 알고리즘 | - |
dc.subject.keywordAuthor | Lane detection | - |
dc.subject.keywordAuthor | 차선 인식 | - |
dc.contributor.affiliatedAuthor | 임용섭 | - |
dc.contributor.affiliatedAuthor | 최경호 | - |
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