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26651 A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior
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Seraj, F. and Zhang, Kui and Türkes, O. and Meratnia, N. and Havinga, P.J.M. (2015) A smartphone based method to enhance road pavement anomaly detection by analyzing the driver behavior. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, 07-11 Sep 2015, Osaka, Japan. pp. 1169-1177. ACM. ISBN 978-1-4503-3575-1

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This paper introduces a method to detect road anomalies by analyzing driver behaviours. The analysis is based on the data and the features extracted from smartphone inertial sensors to calculate the angle of swerving and also based on distinctive states of a driver behaviour event. A novel approach is introduced to deal with the gyroscope drift, reducing the average angle estimation error for curves up to 2° and the overall average angle error up to 5°. Using a simple machine learning approach and a clustering algorithm, the method can detect 70% of the swerves and 95% of the turns on the road.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-General
Research Project:SMART ROAD: Participatory Sensing-based Road Monitoring Using Smart Phones
Uncontrolled Keywords:Road Monitoring, Anomaly Detection, Smartphone Sensing,
Driver Behaviour
ID Code:26651
Deposited On:08 January 2016
More Information:statisticsmetis

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