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Road safety analysis framework based on vehicle vibrations and sounds using deep learning techniques

Author

Listed:
  • Permanki Guthu Rithesh Pakkala

    (Sahyadri College of Engineering and Management, Mangaluru, and Affiliated to Visvesvaraya Technological University)

  • R. Akhila Thejaswi

    (Sahyadri College of Engineering and Management, Mangaluru, and Affiliated to Visvesvaraya Technological University)

  • Bellipady Shamantha Rai

    (Sahyadri College of Engineering and Management, Mangaluru, and Affiliated to Visvesvaraya Technological University)

  • H. R. Nagesh

    (Canara Engineering College, Bantwal and Affiliated to Visvesvaraya Technological University)

Abstract

Road accidents in India occur due to potholes. These potholes are not repaired because the authorities will not be aware of them unless the public raises an issue. The lack of adequate techniques to identify potholes has caused huge trouble to the public. The primary goal of this study is to build a deep learning model that would analyze the patterns in the sound recording of the vehicles and label the Road Anomaly Events (RAEs). Deep learning techniques like Convolutional Neural Network (CNN) and BLSTM are used to classify the sound signature and then labeled it accordingly. The idea can be implemented in areas where there is regular movement of vehicles to identify the exact locations of the pothole and inform the concerned authorities so that the public can experience smoother roads. From the analysis, it is found that the model has an accuracy of 83% with ADAM Optimizer while RMSProp produces 54–60% accuracy.

Suggested Citation

  • Permanki Guthu Rithesh Pakkala & R. Akhila Thejaswi & Bellipady Shamantha Rai & H. R. Nagesh, 2024. "Road safety analysis framework based on vehicle vibrations and sounds using deep learning techniques," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(3), pages 1086-1097, March.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:3:d:10.1007_s13198-023-02191-w
    DOI: 10.1007/s13198-023-02191-w
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