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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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 languageEnglish
Title of host publication2016 IEEE International Conference on Multimedia and Expo, ICME 2016
PublisherIEEE Computer Society
Volume2016-August
ISBN (Electronic)9781467372589
DOIs
Publication statusPublished - 2016 Aug 25
Externally publishedYes
Event2016 IEEE International Conference on Multimedia and Expo, ICME 2016 - Seattle, United States
Duration: 2016 Jul 112016 Jul 15

Other

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

Fingerprint

Recurrent neural networks
Navigation systems
Classifiers
Railroad cars
Feedforward neural networks
Sensors
Logistics
Detectors
Long short-term memory

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

Hori, C., Watanabe, S., Hori, T., Harsham, B. A., Hershey, J., Koji, Y., ... Furumoto, Y. (2016). Driver confusion status detection using recurrent neural networks. In 2016 IEEE International Conference on Multimedia and Expo, ICME 2016 (Vol. 2016-August). [7552966] IEEE Computer Society. https://doi.org/10.1109/ICME.2016.7552966

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 proceedingConference contribution

Hori, C, Watanabe, S, Hori, T, Harsham, BA, Hershey, J, Koji, Y, Fujii, Y & Furumoto, Y 2016, Driver confusion status detection using recurrent neural networks. in 2016 IEEE International Conference on Multimedia and Expo, ICME 2016. vol. 2016-August, 7552966, IEEE Computer Society, 2016 IEEE International Conference on Multimedia and Expo, ICME 2016, Seattle, United States, 16/7/11. https://doi.org/10.1109/ICME.2016.7552966
Hori C, Watanabe S, Hori T, Harsham BA, Hershey J, Koji Y et al. Driver confusion status detection using recurrent neural networks. In 2016 IEEE International Conference on Multimedia and Expo, ICME 2016. Vol. 2016-August. IEEE Computer Society. 2016. 7552966 https://doi.org/10.1109/ICME.2016.7552966
Hori, Chiori ; Watanabe, Shinji ; Hori, Takaaki ; Harsham, Bret A. ; Hershey, Johnr ; Koji, Yusuke ; Fujii, Yoichi ; Furumoto, Yuki. / Driver confusion status detection using recurrent neural networks. 2016 IEEE International Conference on Multimedia and Expo, ICME 2016. Vol. 2016-August IEEE Computer Society, 2016.
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