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Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors

Author

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  • Aya Hamdy Ali

    (Helwan University, HCI-Lab, Faculty of Computer Science and Information Systems, Cairo, Egypt)

  • Ayman Atia

    (Helwan University, HCI-Lab, Faculty of Computer Science and Information Systems, Cairo, Egypt)

  • Mostafa-Sami M. Mostafa

    (Helwan University, HCI-Lab, Faculty of Computer Science and Information Systems, Cairo, Egypt)

Abstract

Road traffic accidents are caused 1.25 million deaths per year worldwide. To improve road safety and reducing road accidents, a recognition method for driving events is introduced in this paper. The proposed method detected and classified both driving behaviors and road anomalies patterns based on smartphone sensors (accelerometer and gyroscope). k-Nearest Neighbor and Dynamic Time Warping algorithms were utilized for method evaluation. Experiments were conducted to evaluate k-nearest neighbor and dynamic time warping algorithms accuracy for road anomalies and driving behaviors detection, moreover, driving behaviors classification. Evaluation results showed that k-nearest neighbor algorithm detected road anomalies and driving behaviors with total accuracy 98.67%. Dynamic time warping algorithm classified (normal and abnormal) driving behaviors with total accuracy 96.75%.

Suggested Citation

  • Aya Hamdy Ali & Ayman Atia & Mostafa-Sami M. Mostafa, 2017. "Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(3), pages 22-37, July.
    • Handle: RePEc:igg:jaci00:v:8:y:2017:i:3:p:22-37
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    Cited by:

    1. Neeraj Gupta & Saurabh Gupta & Mahdi Khosravy & Nilanjan Dey & Nisheeth Joshi & Rubén González Crespo & Nilesh Patel, 2021. "RETRACTED ARTICLE: Economic IoT strategy: the future technology for health monitoring and diagnostic of agriculture vehicles," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1117-1128, April.

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