Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification
Joint Authors
Thajchayapong, Suttipong
Tanprasert, Thitaree
Saiprasert, Chalermpol
Source
Journal of Advanced Transportation
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-16
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This paper proposes an algorithm for real-time driver identification using the combination of unsupervised anomaly detection and neural networks.
The proposed algorithm uses nonphysiological signals as input, namely, driving behavior signals from inertial sensors (e.g., accelerometers) and geolocation signals from GPS sensors.
First anomaly detection is performed to assess if the current driver is whom he/she claims to be.
If an anomaly is detected, the algorithm proceeds to find relevant features in the input signals and use neural networks to identify drivers.
To assess the proposed algorithm, real-world data are collected from ten drivers who drive different vehicles on several routes in real-world traffic conditions.
Driver identification is performed on each of the seven-second-long driving behavior signals and geolocation signals in a streaming manner.
It is shown that the proposed algorithm can achieve relatively high accuracy and identify drivers within 13 seconds.
The proposed algorithm also outperforms the previously proposed driver identification algorithms.
Furthermore, to demonstrate how the proposed algorithm can be deployed in real-world applications, results from real-world data associated with each operation of the proposed algorithm are shown step-by-step.
American Psychological Association (APA)
Tanprasert, Thitaree& Saiprasert, Chalermpol& Thajchayapong, Suttipong. 2017. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification. Journal of Advanced Transportation،Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1170828
Modern Language Association (MLA)
Tanprasert, Thitaree…[et al.]. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification. Journal of Advanced Transportation No. 2017 (2017), pp.1-13.
https://search.emarefa.net/detail/BIM-1170828
American Medical Association (AMA)
Tanprasert, Thitaree& Saiprasert, Chalermpol& Thajchayapong, Suttipong. Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification. Journal of Advanced Transportation. 2017. Vol. 2017, no. 2017, pp.1-13.
https://search.emarefa.net/detail/BIM-1170828
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1170828