376 0

LSTM 기반 Model-Free LKAS 조향 각 생성

Title
LSTM 기반 Model-Free LKAS 조향 각 생성
Other Titles
LSTM Based Model-Free LKAS Steering Angle Generation
Author
박장현
Keywords
Long Short-Term Memory (LSTM); Recurrent Neural Network (RNN); steering angle prediction; Lane Kepping Assistance System (LKAS)
Issue Date
2017-09
Publisher
제어·로봇·시스템학회
Citation
제어.로봇.시스템학회 논문지, v. 23, no. 9, page. 732-739
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).
URI
http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE07230998&language=ko_KRhttps://repository.hanyang.ac.kr/handle/20.500.11754/115631
ISSN
1976-5622; 2233-4335
DOI
10.5302/J.ICROS.2017.17.0097
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE