Human driving patterns are individualized and distinct, and the vehicle fingerprint of drivers could aid in automatically identifying different driving behaviors and be used in fields such as anti-theft systems. The introduction of in-vehicle networking systems, along with cutting-edge sensors and communication technologies have enabled the collection of massive volumes of nearly real-time data about vehicles and drivers, thereby expanding future possibilities. This research work presents a Deep learning-based LSTM strategy to classify the drivers based on the driving characteristics of the drivers collected by OBD-II protocols from vehicles’ ECUs. In contrast to previous research, a cross-validation technique is employed to obtain reliable results when applied to actual data. An accuracy of above 99 percent has been obtained by assessing a dataset comprised of the data gathered from actual vehicles. The work demonstrated a better performance in terms of evaluation metrics (Accuracy, Precision, Recall, and F-1 Score) when compared with other Machine Learning (ML) and Deep Learning (DL) models.


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    Title :

    Effective Long Short-Term Memory based-Driver Identification in ITS


    Contributors:


    Publication date :

    2022-07-20


    Size :

    1278699 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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