In order to study the technical issues of traffic safety early warning on roads with poor sight distance, we first collect the sounds of vehicles running on the road, select representative vehicle sounds as recognition objects, pre-emphasize the vehicle sound signals, add windows into frames, and calculate the power spectrum. Input the Mel filter bank to obtain the MFCC of the vehicle sound signal and construct a feature vector with its characteristic values; then the BP neural network algorithm is improved to classify and identify the vehicle signal, thereby achieving the purpose of vehicle identification. Experiments show that the accuracy of the proposed voice recognition technology reaches more than 90%. This technology can be applied to road sections with poor sight distance to identify passing vehicles.


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    Titel :

    Research on Safety Early Warning Technology for Road Sections with Poor Sight Distance based on Acoustic Signals


    Weitere Titelangaben:

    Advances in Engineering res


    Beteiligte:
    Zhao, Gaofeng (Herausgeber:in) / Satyanaga, Alfrendo (Herausgeber:in) / Ramani, Sujatha Evangelin (Herausgeber:in) / Abdel Raheem, Shehata E. (Herausgeber:in) / Cheng, Xiaonan (Autor:in) / Chen, Xin (Autor:in) / Li, Jian (Autor:in) / Song, Jielong (Autor:in) / Tang, Xiankang (Autor:in)

    Kongress:

    International Symposium on Traffic Transportation and Civil Architecture ; 2024 ; Tianjin, China June 21, 2024 - June 23, 2024



    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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