Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정정주 | - |
dc.date.accessioned | 2020-10-13T00:22:39Z | - |
dc.date.available | 2020-10-13T00:22:39Z | - |
dc.date.issued | 2019-10 | - |
dc.identifier.citation | 2019 19th International Conference on Control, Automation and Systems (ICCAS), Page. 706-711 | en_US |
dc.identifier.isbn | 978-89-93215-17-5 | - |
dc.identifier.issn | 2642-3901 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/8971631 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/154535 | - |
dc.description.abstract | In this paper, we propose an algorithm compensating for an unmatched disturbance which is difficult to thoroughly remove. In order to compensate for the disturbance of an autonomous driving vehicle, we exploit Deep Neural Network(DNN). It is well-known that we cannot compute the control command that completely compensates for any unmatched disturbance. In vehicle lateral dynamics using a lane keeping system with a look-ahead distance, there always exists the disturbance caused by a curvature of road. Thus, it is inevitable that the lane keeping system only with a feedback control has steady-state error in the lane center on a curved road. To resolve this problem, we utilize the feedback control system with DNN. The proposed method is unique in compensating for the unmatched disturbance in lane keeping system. From a simulation study, we observed that the proposed method could reduce lateral tracking error up to a half on a curved road. | en_US |
dc.description.sponsorship | This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.R7117-16-0164, Development of wide area driving environment awareness and cooperative driving technology which are based on V2X wireless communication), and by Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE) (No.1007670, AI-based Driving Risk Assessment and Optimal ADAS/Chassis Control). | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.subject | Autonomous driving | en_US |
dc.subject | Lateral motion model | en_US |
dc.subject | Lane Keeping System(LKS) | en_US |
dc.subject | Deep Neural Network(DNN) | en_US |
dc.subject | Deep learning | en_US |
dc.title | Autonomous Driving Vehicles with Unmatched Disturbance Compensation using Deep Neural Networks | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.23919/ICCAS47443.2019.8971631 | - |
dc.relation.page | 706-711 | - |
dc.contributor.googleauthor | Kim, Jin Sung | - |
dc.contributor.googleauthor | Kim, Dae Jung | - |
dc.contributor.googleauthor | Lee, Seung-Hi | - |
dc.contributor.googleauthor | Chung, Chung Choo | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DIVISION OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | cchung | - |
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