Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 |
| Publisher | IEEE Computer Society |
| Volume | 2016-August |
| ISBN (Electronic) | 9781467372589 |
| DOIs | |
| Publication status | Published - 2016 Aug 25 |
| Externally published | Yes |
| Event | 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 - Seattle, United States Duration: 2016 Jul 11 → 2016 Jul 15 |
Other
| Other | 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 |
|---|---|
| Country | United States |
| City | Seattle |
| Period | 16/7/11 → 16/7/15 |
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Keywords
- driver confusion status prediction
- long short-term memory
- multimodal processing
- recurrent neural network
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
Cite this
Driver confusion status detection using recurrent neural networks. / Hori, Chiori; Watanabe, Shinji; Hori, Takaaki; Harsham, Bret A.; Hershey, Johnr; Koji, Yusuke; Fujii, Yoichi; Furumoto, Yuki.
2016 IEEE International Conference on Multimedia and Expo, ICME 2016. Vol. 2016-August IEEE Computer Society, 2016. 7552966.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Driver confusion status detection using recurrent neural networks
AU - Hori, Chiori
AU - Watanabe, Shinji
AU - Hori, Takaaki
AU - Harsham, Bret A.
AU - Hershey, Johnr
AU - Koji, Yusuke
AU - Fujii, Yoichi
AU - Furumoto, Yuki
PY - 2016/8/25
Y1 - 2016/8/25
N2 - 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.
AB - 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.
KW - driver confusion status prediction
KW - long short-term memory
KW - multimodal processing
KW - recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=84987606048&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84987606048&partnerID=8YFLogxK
U2 - 10.1109/ICME.2016.7552966
DO - 10.1109/ICME.2016.7552966
M3 - Conference contribution
AN - SCOPUS:84987606048
VL - 2016-August
BT - 2016 IEEE International Conference on Multimedia and Expo, ICME 2016
PB - IEEE Computer Society
ER -