Maintaining the quality of roadways is a major challenge.Maintenance work of roads is although necessary but can lead to traffic jams, causing frustration among road users, many a time leads to unwanted consequences. In order to avoid problems related with road anomalies, system has been proposed that can detect road anomalies using a hardware setup. This system that is based on data collected by the hardware setup, automatically detects and classifies road anomalies. Major focus is on the methodology and the process that has been used for the data collection, and discuss some problems and challenges resulting from the above-described process. From a traffic operation perspective, establishing a reliable transportation system could benefit both system planners and the end user. Developing an effective road anomaly detection system will surely improve the viability of autonomous vehicles and will also contribute toward the reduction of road anomalies related accidents. Out of different machine learning methods that has been used to train and test the model, support vector machine (SVM) has shown as shown best accuracy of 95.2% on the self-generated dataset.


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    Titel :

    Machine Learning Assisted MPU6050-Based Road Anomaly Detection


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Kumar Singh, Koushlendra (Herausgeber:in) / Bajpai, Manish Kumar (Herausgeber:in) / Sheikh Akbari, Akbar (Herausgeber:in) / Tripathi, Jyoti (Autor:in) / Kumar, Bijendra (Autor:in)


    Erscheinungsdatum :

    2023-05-01


    Format / Umfang :

    15 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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