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Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors

Aya Hamdy Ali, Ayman Atia, Mostafa-Sami M. Mostafa

Source Title: International Journal of Ambient Computing and Intelligence (IJACI)8(3)

ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781522523017|DOI: 10.4018/IJACI.2017070102

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MLA

Ali, Aya Hamdy, et al. "Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors." IJACI vol.8, no.3 2017: pp.22-37. http://doi.org/10.4018/IJACI.2017070102

APA

Ali, A. H., Atia, A., & Mostafa, M. M. (2017). Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors. International Journal of Ambient Computing and Intelligence (IJACI), 8(3), 22-37. http://doi.org/10.4018/IJACI.2017070102

Chicago

Ali, Aya Hamdy, Ayman Atia, and Mostafa-Sami M. Mostafa. "Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors," International Journal of Ambient Computing and Intelligence (IJACI) 8, no.3: 22-37. http://doi.org/10.4018/IJACI.2017070102

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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%.

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Recognizing Driving Behavior and Road Anomaly using Smartphone Sensors

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