Driver confusion status detection using recurrent neural networks

Chiori Hori, Shinji Watanabe, Takaaki Hori, Bret A. Harsham, Johnr Hershey, Yusuke Koji, Yoichi Fujii, Yuki Furumoto

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

In this paper, we present a method for estimating the confusion level of a driver using a classifier trained on multimodal sensor data. Using the driver confusion status detector, a car navigation system can proactively support the driver when he/she is confused. A corpus of data was collected during on-road driving in traffic using a navigation system and a car instrumented with a variety of sensors. The data was manually annotated with the driver's confusion status and with multiple features representing driver's behavior and the traffic conditions. We compared different types of classifiers trained from the data: logistic regression, a feed-forward neural network, a recurrent neural networks, and a long short-term memory (LSTM)-based recurrent neural network. The accuracy was evaluated using F-max as well as precision/recall. We found that the LSTM outperformed the other models.

本文言語English
ホスト出版物のタイトル2016 IEEE International Conference on Multimedia and Expo, ICME 2016
出版社IEEE Computer Society
2016-August
ISBN(電子版)9781467372589
DOI
出版ステータスPublished - 2016 8 25
外部発表はい
イベント2016 IEEE International Conference on Multimedia and Expo, ICME 2016 - Seattle, United States
継続期間: 2016 7 112016 7 15

Other

Other2016 IEEE International Conference on Multimedia and Expo, ICME 2016
国/地域United States
CitySeattle
Period16/7/1116/7/15

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用

フィンガープリント

「Driver confusion status detection using recurrent neural networks」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル