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Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification
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Document Title
Combining Unsupervised Anomaly Detection and Neural Networks for Driver Identification
Author
Tanprasert T., Saiprasert C., Thajchayapong S.
Name from Authors Collection
Affiliations
Department of Computer Science, Harvey Mudd College, Claremont, CA, United States; National Electronic and Computer Technology Center (NECTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani, Thailand
Type
Article
Source Title
Journal of Advanced Transportation
ISSN
01976729
Year
2017
Volume
2017
Open Access
All Open Access, Gold, Green
Publisher
Hindawi Limited
DOI
10.1155/2017/6057830
Format
Abstract
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. © 2017 Thitaree Tanprasert et al.
Industrial Classification
Knowledge Taxonomy Level 1
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Knowledge Taxonomy Level 3
License
N/A
Rights
N/A
Publication Source
Scopus