A real-time method of identifying road unevenness with serial acceleration signals is proposed. This new method uses Long Short-Term Memory (LSTM) network to identify the characteristics of signal in time domain and classify it into different unevenness classes. A proving ground experiment is designed and conducted to construct and validate the algorithm. The good estimation accuracy, as well as robustness, is realized at different vehicle velocities and suspension damping levels. The identification results will provide an important basis for the control of connected vehicles.


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    Titel :

    Acceleration Based Intelligent Real-Time Road Identification for Connected Vehicles


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zhang, Zhenyuan (Herausgeber:in) / Liang, Guanqun (Autor:in) / Zhao, Tong (Autor:in) / Li, Ningfei (Autor:in) / Shangguan, Zhengwei (Autor:in) / Du, Yongchang (Autor:in) / Wei, Yintao (Autor:in)

    Kongress:

    International Conference on Intelligent Transportation Engineering ; 2021 ; Beijing, China October 29, 2021 - October 31, 2021



    Erscheinungsdatum :

    01.06.2022


    Format / Umfang :

    10 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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