IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2021i1p348-d713997.html
   My bibliography  Save this article

Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion

Author

Listed:
  • Yongfeng Ma

    (Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China)

  • Zhuopeng Xie

    (Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China)

  • Shuyan Chen

    (Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China)

  • Ying Wu

    (Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 211189, China)

  • Fengxiang Qiao

    (Innovative Transportation Research Institute, Texas Southern University, Houston, TX 77004, USA)

Abstract

Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.

Suggested Citation

  • Yongfeng Ma & Zhuopeng Xie & Shuyan Chen & Ying Wu & Fengxiang Qiao, 2021. "Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion," IJERPH, MDPI, vol. 19(1), pages 1-14, December.
  • Handle: RePEc:gam:jijerp:v:19:y:2021:i:1:p:348-:d:713997
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/1/348/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/1/348/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Yingshi & Zhang, Hongjia & Wang, Chang & Sun, Qinyu & Li, Wanmin, 2021. "Driver lane change intention recognition in the connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(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. Wang, Lichao & Yang, Min & Li, Ye & Hou, Yiqi, 2022. "A model of lane-changing intention induced by deceleration frequency in an automatic driving environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    2. Xin Chang & Xingjian Zhang & Haichao Li & Chang Wang & Zhe Liu, 2022. "A Survey on Mixed Traffic Flow Characteristics in Connected Vehicle Environments," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    3. Yuan, Renteng & Abdel-Aty, Mohamed & Gu, Xin & Zheng, Ou & Xiang, Qiaojun, 2023. "A unified modeling framework for lane change intention recognition and vehicle status prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).

    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:gam:jijerp:v:19:y:2021:i:1:p:348-:d:713997. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.