Abnormal driving behaviours, such as rapid acceleration, emergency braking, and rapid lane changing, bring great uncertainty to traffic, and can easily lead to traffic accidents. The accurate identification of abnormal driving behaviour helps to judge the driver's driving style, inform surrounding vehicles, and ensure the road traffic safety. Most of the existing studies use clustering and shallow learning, it is difficult to accurately identify the types of abnormal driving behaviours. Aimed at addressing the difficulty of identifying driving behaviour, this study proposed a recognition model based on a long short-term memory network and convolutional neural network (LSTM-CNN). The extreme acceleration and deceleration points are detected through the statistical analysis of real vehicle driving data, and the driving behaviour recognition data set is established. By using the data set to train the model, the LSTM-CNN can achieve a better result.


    Zugriff

    Zugriff über TIB


    Exportieren, teilen und zitieren



    Titel :

    Long short-term memory and convolutional neural network for abnormal driving behaviour recognition


    Beteiligte:
    Jia, Shuo (Autor:in) / Hui, Fei (Autor:in) / Li, Shining (Autor:in) / Zhao, Xiangmo (Autor:in) / Khattak, Asad J. (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2019-07-24


    Format / Umfang :

    7 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Long short‐term memory and convolutional neural network for abnormal driving behaviour recognition

    Shuo Jia / Fei Hui / Shining Li et al. | DOAJ | 2020

    Freier Zugriff

    Long short‐term memory and convolutional neural network for abnormal driving behaviour recognition

    Jia, Shuo / Hui, Fei / Li, Shining et al. | Wiley | 2020

    Freier Zugriff



    Short-Term Passenger Flow Prediction Using a Bus Network Graph Convolutional Long Short-Term Memory Neural Network Model

    Baghbani, Asiye / Bouguila, Nizar / Patterson, Zachary | Transportation Research Record | 2022

    Freier Zugriff