The vehicle classification system developed by Federal Highway Administration (FHWA) of United States divides vehicle type into 13 categories depending on the number of axles and the wheelbase. However, establishing a fixed threshold for classifying a vehicle is difficult. The overlapping between vehicles pattern in the system needs a pattern recognition technique to distinguish between different vehicle categories. In this study, machine learning algorithms were used to classify various vehicles based on the collected traffic data from the embedded three-dimension Glass Fiber-Reinforced Polymer packaged Fiber Bragg Grating sensors (3D GFRP-FBG). The investigated machine learning algorithms include the support vector machines (SVM), Neural Network, and k-nearest neighbors (KNN) algorithms.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Road vehicle classification using machine learning techniques


    Beteiligte:
    Al-Tarawneh, Mu'ath (Autor:in) / Huang, Ying (Autor:in)

    Kongress:

    Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2019 ; 2019 ; Denver,Colorado,United States


    Erschienen in:

    Proc. SPIE ; 10970


    Erscheinungsdatum :

    2019-03-27





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Road vehicle classification using machine learning techniques

    Al-Tarawneh, Mu'ath / Huang, Ying | British Library Conference Proceedings | 2019


    Comparison of Machine Learning Techniques for Vehicle Classification Using Road Side Sensors

    Kleyko, Denis / Hostettler, Roland / Birk, Wolfgang et al. | IEEE | 2015


    Road marking detection and classification using machine learning algorithms

    Chen, Tairui / Chen, Zhilu / Shi, Quan et al. | IEEE | 2015


    Combat vehicle classification using machine learning

    Zeng, H. / Huang, J. / Liang, Y. | Tema Archiv | 1999


    ADS-B Attack Classification using Machine Learning Techniques

    Kacem, Thabet / Kaya, Aydin / Seydi Keceli, Ali et al. | IEEE | 2021