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Mobile Information Systems
Volume 2017 (2017), Article ID 9075653, 15 pages
Research Article

DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration

1School of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
2School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

Correspondence should be addressed to Huazhi Sun

Received 9 November 2016; Accepted 21 February 2017; Published 22 March 2017

Academic Editor: Francesco Palmieri

Copyright © 2017 Chunmei Ma et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.