Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Seo, Eunbin | - |
dc.contributor.author | Lee, Seunggi | - |
dc.contributor.author | Shin, Gwanjun | - |
dc.contributor.author | Yeo, Hoyeong | - |
dc.contributor.author | Lim, Yongseob | - |
dc.contributor.author | Choi, Gyeungho | - |
dc.date.accessioned | 2021-06-07T20:04:38Z | - |
dc.date.available | 2021-06-07T20:04:38Z | - |
dc.date.created | 2021-05-27 | - |
dc.date.issued | 2021-05 | - |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/13692 | - |
dc.description.abstract | Path tracking system plays a key technology in autonomous driving. The system should be driven accurately along the lane and be careful not to cause any inconvenience to passengers. To address such tasks, this research proposes hybrid tracker based optimal path tracking system. By applying a deep learning based lane detection algorithm and a designated fast lane fitting algorithm, this research developed a lane processing algorithm that shows a match rate with actual lanes with minimal computational cost. In addition, three modified path tracking algorithms were designed using the GPS based path or the vision based path. In the driving system, a match rate for the correct ideal path does not necessarily represent driving stability. This research proposes hybrid tracker based optimal path tracking system by applying the concept of an observer that selects the optimal tracker appropriately in complex road environments. The driving stability has been studied in complex road environments such as straight road with multiple 3-way junctions, roundabouts, intersections, and tunnels. Consequently, the proposed system experimentally showed the high performance with consistent driving comfort by maintaining the vehicle within the lanes accurately even in the presence of high complexity of road conditions. Code will be available in https://github.com/DGIST-ARTIV. CCBY | - |
dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Hybrid Tracker Based Optimal Path Tracking System of Autonomous Driving for Complex Road Environments | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ACCESS.2021.3078849 | - |
dc.identifier.scopusid | 2-s2.0-85105876994 | - |
dc.identifier.bibliographicCitation | IEEE Access, v.9, pp.71763 - 71777 | - |
dc.description.isOpenAccess | TRUE | - |
dc.subject.keywordAuthor | Roads | - |
dc.subject.keywordAuthor | Stability analysis | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Autonomous vehicles | - |
dc.subject.keywordAuthor | Global Positioning System | - |
dc.subject.keywordAuthor | Lane detection | - |
dc.subject.keywordAuthor | Computational modeling | - |
dc.subject.keywordAuthor | Intelligent vehicles | - |
dc.subject.keywordAuthor | vehicle driving | - |
dc.subject.keywordAuthor | autonomous vehicles | - |
dc.subject.keywordAuthor | path tracking | - |
dc.subject.keywordAuthor | lane detection | - |
dc.subject.keywordAuthor | driving stability | - |
dc.subject.keywordPlus | Road environment | - |
dc.subject.keywordPlus | Tracking (position) | - |
dc.subject.keywordPlus | Autonomous vehicles | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Roads and streets | - |
dc.subject.keywordPlus | Autonomous driving | - |
dc.subject.keywordPlus | Computational costs | - |
dc.subject.keywordPlus | Driving stability | - |
dc.subject.keywordPlus | Driving systems | - |
dc.subject.keywordPlus | Fitting algorithms | - |
dc.subject.keywordPlus | Key technologies | - |
dc.subject.keywordPlus | Processing algorithms | - |
dc.citation.endPage | 71777 | - |
dc.citation.startPage | 71763 | - |
dc.citation.title | IEEE Access | - |
dc.citation.volume | 9 | - |
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