DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration
Joint Authors
Dai, Xili
Zhu, Jinqi
Sun, Huazhi
Liu, Nianbo
Ma, Chunmei
Liu, Ming
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-22
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Telecommunications Engineering
Abstract EN
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.
American Psychological Association (APA)
Ma, Chunmei& Dai, Xili& Zhu, Jinqi& Liu, Nianbo& Sun, Huazhi& Liu, Ming. 2017. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems،Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1189249
Modern Language Association (MLA)
Ma, Chunmei…[et al.]. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems No. 2017 (2017), pp.1-15.
https://search.emarefa.net/detail/BIM-1189249
American Medical Association (AMA)
Ma, Chunmei& Dai, Xili& Zhu, Jinqi& Liu, Nianbo& Sun, Huazhi& Liu, Ming. DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration. Mobile Information Systems. 2017. Vol. 2017, no. 2017, pp.1-15.
https://search.emarefa.net/detail/BIM-1189249
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189249