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    Abnormal Driving Behavior Identification Based on Naturalistic Driving Data Using LSTM Recurrent Neural Networks

    Abnormal Driving Behavior Identification Based on Naturalistic Driving Data Using LSTM Recurrent Neural Networks


    This paper presents a classification model to identify abnormal driving behavior on roads. Vehicle dynamics is considered. A LSTM recurrent neural network model-based is applied. The vehicle dynamics features are measured by smartphone inertial sensors. The real data obtained from the GPS, accelerometer, and gyroscope are used to classify the driving maneuvers. A conventional two-lane and a highway roads located in the Madrid Region, Spain, are used for this research. The results obtained with the proposed model are promising and suggest that this intelligent system can be used to warn drivers of a defective or distracted maneuver in real time, aiming at a safer and more comfortable driving.

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    Titel :

    Abnormal Driving Behavior Identification Based on Naturalistic Driving Data Using LSTM Recurrent Neural Networks

    Weitere Titelangaben:

    Lect. Notes in Networks, Syst.

    Beteiligte:
    García Bringas, Pablo (Herausgeber:in) / Pérez García, Hilde (Herausgeber:in) / Martinez-de-Pison, Francisco Javier (Herausgeber:in) / Villar Flecha, José Ramón (Herausgeber:in) / Troncoso Lora, Alicia (Herausgeber:in) / de la Cal, Enrique A. (Herausgeber:in) / Herrero, Álvaro (Herausgeber:in) / Martínez Álvarez, Francisco (Herausgeber:in) / Psaila, Giuseppe (Herausgeber:in) / Quintián, Héctor (Herausgeber:in)
    Kongress:

    International Workshop on Soft Computing Models in Industrial and Environmental Applications ; 2022 ; Bilbao, Spain September 05, 2022 - September 07, 2022

    Erschienen in:

    17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022) ; Kapitel : 42 ; 435-443

    Lecture Notes in Networks and Systems ; 531

    Erscheinungsdatum :

    2022-10-12

    Format / Umfang :

    9 pages

    ISBN :

    978-3-031-18050-7 , 978-3-031-18049-1

    ISSN :

    2367-3389 , 2367-3370

    DOI :

    https://doi.org/10.1007/978-3-031-18050-7_42

    Medientyp :

    Aufsatz/Kapitel (Buch)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    LSTM , Neural networks , Smartphone sensors , Driving , Identification , Classification , Two-lane road , Highway road Engineering , Computational Intelligence , Industrial and Production Engineering

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