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 |
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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 |
巻 | 2016-August |
ISBN(電子版) | 9781467372589 |
DOI | |
出版ステータス | Published - 2016 8月 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月 11 → 2016 7月 15 |
Other
Other | 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 |
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国/地域 | United States |
City | Seattle |
Period | 16/7/11 → 16/7/15 |
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