Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 박장현 | - |
dc.date.accessioned | 2019-11-30T18:18:51Z | - |
dc.date.available | 2019-11-30T18:18:51Z | - |
dc.date.issued | 2017-09 | - |
dc.identifier.citation | 제어.로봇.시스템학회 논문지, v. 23, no. 9, page. 732-739 | en_US |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.issn | 2233-4335 | - |
dc.identifier.uri | http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07230998&language=ko_KR | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/115631 | - |
dc.description.abstract | In this paper, we propose a lateral upper controller that predicts vehicle motion and generates a model-free LKAS steering angle by applying long-term memory (LSTM), one of the deep learning techniques. The apparent and distinct advantage of this LSTM model is that the relationship of nonlinear sensor data can be grasped and learned devoid of a mathematical model while using the time information. In this sense, the LKAS steering angle can be generated by predicting the movement of the vehicle in consideration of the past vehicle motion based on the sensor data. In addition, the time delay problem due to the difference of sampling time on each sensor can be simply solved by learning based on a time table which has a synchronized sampling time. The input values of the upper controller are the coefficient of the road model and the vehicle dynamic characteristics are obtained from the image processing data from the camera sensor. As for the target values, the steering angles in the next state are selected. The learning model was developed by learning the many-to-one LSTM prediction model with serial connection of LSTM and Fully-Connected (FC) Multilayer Perceptron (MLP). For implementation of the learning model, Tensorflow is employed and the data from a real test road was used. With this model, the learning is conducted and its effectiveness is shown by comparing with a LKAS lateral controller using a model-based multi rate Kalman Filter (MKF). | en_US |
dc.description.sponsorship | 본 연구는 미래창조과학부 및 정보통신기술진흥센터의 대학 ICT연구센터 육성 지원 사업의 연구결과로 수행되었음.(IITP-2017-2012-0-00628) | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 제어·로봇·시스템학회 | en_US |
dc.subject | Long Short-Term Memory (LSTM) | en_US |
dc.subject | Recurrent Neural Network (RNN) | en_US |
dc.subject | steering angle prediction | en_US |
dc.subject | Lane Kepping Assistance System (LKAS) | en_US |
dc.title | LSTM 기반 Model-Free LKAS 조향 각 생성 | en_US |
dc.title.alternative | LSTM Based Model-Free LKAS Steering Angle Generation | en_US |
dc.type | Article | en_US |
dc.relation.no | 9 | - |
dc.relation.volume | 23 | - |
dc.identifier.doi | 10.5302/J.ICROS.2017.17.0097 | - |
dc.relation.page | 732-739 | - |
dc.relation.journal | 제어.로봇.시스템학회 논문지 | - |
dc.contributor.googleauthor | 김현우 | - |
dc.contributor.googleauthor | 박장현 | - |
dc.contributor.googleauthor | Kim, Hyun-Woo | - |
dc.contributor.googleauthor | Park, Jahng-Hyon | - |
dc.relation.code | 2017019108 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF AUTOMOTIVE ENGINEERING | - |
dc.identifier.pid | jpark | - |
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