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A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data

Author

Listed:
  • Zuojin Li
  • Qing Yang
  • Shengfu Chen
  • Wei Zhou
  • Liukui Chen
  • Lei Song

Abstract

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.

Suggested Citation

  • Zuojin Li & Qing Yang & Shengfu Chen & Wei Zhou & Liukui Chen & Lei Song, 2019. "A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:9:p:1550147719872452
    DOI: 10.1177/1550147719872452
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    References listed on IDEAS

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    1. Van Quan Nguyen & Tien Nguyen Anh & Hyung-Jeong Yang, 2019. "Real-time event detection using recurrent neural network in social sensors," International Journal of Distributed Sensor Networks, , vol. 15(6), pages 15501477198, June.
    2. Jaecheul Lee, 2019. "Deep learning–assisted real-time container corner casting recognition," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
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    Cited by:

    1. Zhang, Yu & He, Yingying & Zhang, Likai, 2023. "Recognition method of abnormal driving behavior using the bidirectional gated recurrent unit and convolutional neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    2. Ping-Huan Kuo & Ssu-Ting Lin & Jun Hu, 2020. "DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network," International Journal of Distributed Sensor Networks, , vol. 16(5), pages 15501477209, May.
    3. Kun Liu & Guoqi Feng & Xingyu Jiang & Wenpeng Zhao & Zhiqiang Tian & Rizheng Zhao & Kaihang Bi, 2023. "A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder," Sustainability, MDPI, vol. 15(12), pages 1-25, June.

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