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Recognition method of abnormal driving behavior using the bidirectional gated recurrent unit and convolutional neural network

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  • Zhang, Yu
  • He, Yingying
  • Zhang, Likai

Abstract

Recognition of abnormal driving behavior is an important application area as it can support driving reliability and improve safety. In the last decade, deep learning methods have been presented through fruitful academic research and industrial applications. State-of-the-art deep learning methods are not commonly used for detection of abnormal driving behavior based on driving parameter information, and are lacking in terms of recognition accuracy. Based on this, a novel data-driven abnormal driving behaviors method is proposed in this paper by combining a convolutional neural network (CNN) and a Bidirectional gated recurrent unit (BiGRU). In this process, real vehicle driving data, including the extreme acceleration and steering position, are analyzed to establish a dataset of driving behaviors recognition firstly. Then, the datasets are inputted into the CNN-BiGRU algorithm to recognize the abnormal driving behavior where CNN captures non-linear relations from long-term trends of sequences and BiGRU extracts features of time series from driving parameters. The experimental results show that the proposed method offers improved accuracy and robustness in recognizing abnormal driving compared with other existing machine learning methods.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122008755
    DOI: 10.1016/j.physa.2022.128317
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    References listed on IDEAS

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    1. Chih-Chiang Kuo & Jyun-Naih Lin & Syue-Hua Wu & Cheng-Hsuan Cho & Yi-Hong Chu & Frank Chee Da Tsai, 2014. "Multi-System Integration Scheme for Intelligence Transportation System Applications," International Journal of Wireless Networks and Broadband Technologies (IJWNBT), IGI Global, vol. 3(4), pages 21-35, October.
    2. Xing, Yang & Lv, Chen & Cao, Dongpu & Lu, Chao, 2020. "Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling," Applied Energy, Elsevier, vol. 261(C).
    3. 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.
    4. Wei Yuan & Zhuofan Liu & Rui Fu, 2018. "Predicting Drivers’ Eyes-Off-Road Duration in Different Driving Scenarios," Discrete Dynamics in Nature and Society, Hindawi, vol. 2018, pages 1-9, November.
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    Cited by:

    1. Ma, Changxi & Liu, Tao, 2024. "Demand forecasting of shared bicycles based on combined deep learning models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 635(C).

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