A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data
The study of the robust fatigue feature learning method for the driver’s operational behavior is of great significance for improving the performance of the real-time detection system for driver’s fatigue state. Aiming at how to extract more abstract and deep features in the driver’s direction operat...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-09-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147719872452 |
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| _version_ | 1849684108791250944 |
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| author | Zuojin Li Qing Yang Shengfu Chen Wei Zhou Liukui Chen Lei Song |
| author_facet | Zuojin Li Qing Yang Shengfu Chen Wei Zhou Liukui Chen Lei Song |
| author_sort | Zuojin Li |
| collection | DOAJ |
| description | The study of the robust fatigue feature learning method for the driver’s operational behavior is of great significance for improving the performance of the real-time detection system for driver’s fatigue state. Aiming at how to extract more abstract and deep features in the driver’s direction operation data in the robust feature learning, this article constructs a fuzzy recurrent neural network model, which includes input layer, fuzzy layer, hidden layer, and output layer. The steering-wheel direction sensing time series sends the time series to the input layer through a fixed time window. After the fuzzification process, it is sent to the hidden layer to share the weight of the hidden layer, realize the memorization of the fatigue feature, and improve the feature depth capability of the steering wheel angle time sequence. The experimental results show that the proposed model achieves an average recognition rate of 87.30% in the fatigue sample database of real vehicle conditions, which indicates that the model has strong robustness to different subjects under real driving conditions. The model proposed in this article has important theoretical and engineering significance for studying the prediction of fatigue driving under real driving conditions. |
| format | Article |
| id | doaj-art-cb5626b35ff34a0e9540bbbd6725c103 |
| institution | DOAJ |
| issn | 1550-1477 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-cb5626b35ff34a0e9540bbbd6725c1032025-08-20T03:23:34ZengWileyInternational Journal of Distributed Sensor Networks1550-14772019-09-011510.1177/1550147719872452A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor dataZuojin Li0Qing Yang1Shengfu Chen2Wei Zhou3Liukui Chen4Lei Song5College of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, ChinaCollege of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, ChinaCollege of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, ChinaCollege of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, ChinaCollege of Electrical and Information Engineering, Chongqing University of Science and Technology, Chongqing, ChinaSchool of Computing and Information Technology, Unitec Institute of Technology, Auckland, New ZealandThe study of the robust fatigue feature learning method for the driver’s operational behavior is of great significance for improving the performance of the real-time detection system for driver’s fatigue state. Aiming at how to extract more abstract and deep features in the driver’s direction operation data in the robust feature learning, this article constructs a fuzzy recurrent neural network model, which includes input layer, fuzzy layer, hidden layer, and output layer. The steering-wheel direction sensing time series sends the time series to the input layer through a fixed time window. After the fuzzification process, it is sent to the hidden layer to share the weight of the hidden layer, realize the memorization of the fatigue feature, and improve the feature depth capability of the steering wheel angle time sequence. The experimental results show that the proposed model achieves an average recognition rate of 87.30% in the fatigue sample database of real vehicle conditions, which indicates that the model has strong robustness to different subjects under real driving conditions. The model proposed in this article has important theoretical and engineering significance for studying the prediction of fatigue driving under real driving conditions.https://doi.org/10.1177/1550147719872452 |
| spellingShingle | Zuojin Li Qing Yang Shengfu Chen Wei Zhou Liukui Chen Lei Song A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data International Journal of Distributed Sensor Networks |
| title | A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data |
| title_full | A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data |
| title_fullStr | A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data |
| title_full_unstemmed | A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data |
| title_short | A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data |
| title_sort | fuzzy recurrent neural network for driver fatigue detection based on steering wheel angle sensor data |
| url | https://doi.org/10.1177/1550147719872452 |
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