MISC

査読有り
2018年2月2日

Convolutional neural network based vehicle turn signal recognition

ICIIBMS 2017 - 2nd International Conference on Intelligent Informatics and Biomedical Sciences
  • Keisuke Yoneda
  • ,
  • Akisue Kuramoto
  • ,
  • Naoki Suganuma

2018-
開始ページ
204
終了ページ
205
記述言語
英語
掲載種別
DOI
10.1109/ICIIBMS.2017.8279693
出版者・発行元
Institute of Electrical and Electronics Engineers Inc.

This Automated driving is an emerging technology in which a car performs recognition, decision making, and control. Recognizing surrounding vehicles is a key technology in order to generate a trajectory of ego vehicle. This paper is focused on detecting a turn signal information as one of the driver's intention for surrounding vehicles. Such information helps to predict their behavior in advance especially about lane change and turn left-or-right on intersection. Using their intension, the automated vehicle is able to generate the safety trajectory before they begin to change their behavior. The proposed method recognizes the turn signal for target vehicle based on mono-camera. It detects lighting state using Convolutional Neural Network, and then calculates a flashing frequency using Fast Fourier Transform.

リンク情報
DOI
https://doi.org/10.1109/ICIIBMS.2017.8279693
URL
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85047433511&origin=inward
ID情報
  • DOI : 10.1109/ICIIBMS.2017.8279693
  • SCOPUS ID : 85047433511

エクスポート
BibTeX RIS