IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v609y2023ics0378437122008755.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122008755
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128317?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nan, Sirui & Tu, Ran & Li, Tiezhu & Sun, Jian & Chen, Haibo, 2022. "From driving behavior to energy consumption: A novel method to predict the energy consumption of electric bus," Energy, Elsevier, vol. 261(PA).
    2. Amirgholy, Mahyar & Gao, H. Oliver, 2023. "Optimal traffic operation for maximum energy efficiency in signal-free urban networks: A macroscopic analytical approach," Applied Energy, Elsevier, vol. 329(C).
    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.
    4. Zepeng Gao & Jianbo Feng & Chao Wang & Yu Cao & Bonan Qin & Tao Zhang & Senqi Tan & Riya Zeng & Hongbin Ren & Tongxin Ma & Youshan Hou & Jie Xiao, 2022. "Research on Vehicle Active Steering Stability Control Based on Variable Time Domain Input and State Information Prediction," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    5. Hou, Zhuoran & Guo, Jianhua & Li, Jihao & Hu, Jinchen & Sun, Wen & Zhang, Yuanjian, 2023. "Exploration the pathways of connected electric vehicle design: A vehicle-environment cooperation energy management strategy," Energy, Elsevier, vol. 271(C).
    6. 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.
    7. Nie, Zifei & Farzaneh, Hooman, 2023. "Energy-efficient lane-change motion planning for personalized autonomous driving," Applied Energy, Elsevier, vol. 338(C).
    8. Zhou, Quan & Li, Yanfei & Zhao, Dezong & Li, Ji & Williams, Huw & Xu, Hongming & Yan, Fuwu, 2022. "Transferable representation modelling for real-time energy management of the plug-in hybrid vehicle based on k-fold fuzzy learning and Gaussian process regression," Applied Energy, Elsevier, vol. 305(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122008755. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.