Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정정주 | - |
dc.date.accessioned | 2022-05-06T00:15:09Z | - |
dc.date.available | 2022-05-06T00:15:09Z | - |
dc.date.issued | 2020-09 | - |
dc.identifier.citation | 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), page. 1851-1856 | en_US |
dc.identifier.isbn | 978-1-7281-4149-7 | - |
dc.identifier.uri | https://xplorestaging.ieee.org/document/9294522?denied= | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/170598 | - |
dc.description.abstract | In this paper, we propose a novel radar-based lane estimation method using Deep Neural Network(DNN) without vision sensors. First, the feature vector is selected using data coming from radar and in-vehicle sensors. The feature vectors are stacked and entered into the network so that the input of the network has spatio-temporal information of the relative motion between the ego vehicle and a leading vehicle. We used a parallel structure of the DNN to estimate the road lane model for the Lane-Keeping System(LKS). The Scaled Conjugate Gradient method is adopted for optimizing the neural network. We performed a comparative study between a vision sensor and the proposed system. From the experiment results, the proposed scheme outperforms the vision system when the vision system becomes failure due to environmental effects such as shadows or lane contamination. It is expected that the proposed method is sufficient to improve the performance of LKS if the proposed system is fused with the vision system for fail-operational lanekeeping system of autonomous highway driving. | en_US |
dc.description.sponsorship | This work was supported by Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (No.20000293, Road Surface Condition Detection using Environmental and In-vehicle Sensors). | en_US |
dc.language.iso | en | en_US |
dc.publisher | 2020 IEEE Intelligent Transportation Systems Conference | en_US |
dc.subject | TRACKING | en_US |
dc.title | Radar-Based Lane Estimation with Deep Neural Network for Lane-Keeping System of Autonomous Highway Driving | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1109/ITSC45102.2020.9294522 | - |
dc.relation.page | 1851-1856 | - |
dc.contributor.googleauthor | Choi, Joo Young | - |
dc.contributor.googleauthor | Kim, Jin Sung | - |
dc.contributor.googleauthor | Chung, Chung Choo | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | SCHOOL OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | cchung | - |
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