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Paper Abstract and Keywords
Presentation 2024-01-18 11:05
Preliminary Study on Driver Drowsiness Detection with Deep Learning Using Vehicular Data
Yutaro Nakagama, Daisuke Ishii (JAIST), Kazuki Yoshizoe (Kyushu Univ.) MSS2023-63 SS2023-42
Abstract (in Japanese) (See Japanese page) 
(in English) Driver drowsiness detection (DDD) systems are being developed to prevent accidents caused by the distraction of automobile drivers. This research aims to develop a highly accurate DDD system based on deep learning using vehicular data. In this study, a driving simulator, CARLA, was used to perform a driving simulation by a subject; then, DDD was performed based on anomaly detection by comparing predicted and acquired yaw rate data. The prediction was based on models generated by deep learning and difference equation models identified by the recurrent least squares method. The precision of anomaly detection for each model was evaluated based on the ROC curves, and the results indicate that the deep learning model was more accurate than the difference equation model.
Keyword (in Japanese) (See Japanese page) 
(in English) driver drowsiness detection / deep learning / / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 334, MSS2023-63, pp. 64-69, Jan. 2024.
Paper # MSS2023-63 
Date of Issue 2024-01-10 (MSS, SS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MSS2023-63 SS2023-42

Conference Information
Committee SS MSS  
Conference Date 2024-01-17 - 2024-01-18 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To MSS 
Conference Code 2024-01-SS-MSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Preliminary Study on Driver Drowsiness Detection with Deep Learning Using Vehicular Data 
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Keyword(1) driver drowsiness detection  
Keyword(2) deep learning  
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1st Author's Name Yutaro Nakagama  
1st Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
2nd Author's Name Daisuke Ishii  
2nd Author's Affiliation Japan Advanced Institute of Science and Technology (JAIST)
3rd Author's Name Kazuki Yoshizoe  
3rd Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2024-01-18 11:05:00 
Presentation Time 25 minutes 
Registration for MSS 
Paper # MSS2023-63, SS2023-42 
Volume (vol) vol.123 
Number (no) no.334(MSS), no.335(SS) 
Page pp.64-69 
#Pages 6 
Date of Issue 2024-01-10 (MSS, SS) 

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