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Driver Identification Based on Wavelet Transform Using Driving Patterns

Authors
Kwak, Byung IlHan, Mee LanKim, Huy Kang
Issue Date
4월-2021
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Driver identification; driving-pattern analysis; machine learning; wavelet transform
Citation
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.17, no.4, pp.2400 - 2410
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume
17
Number
4
Start Page
2400
End Page
2410
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/49436
DOI
10.1109/TII.2020.2999911
ISSN
1551-3203
Abstract
The modern automotive system, based on the convergence of information and communication technologies, is equipped with various functions to ensure vehicle safety and convenience of the driver. A driver-identification technology is an effective method to perform vehicle-theft detection. It can also provide customized driver-personalization services, such as healthcare or insurance. In this article, we propose and evaluate a driver-identification method based on wavelet transform by performing driving-pattern analysis for each driver. We compare the performances of three different machine-learning algorithms, namely Support Vector Machine (SVM), Random Forest, and XGBoost for performing driver identification. The proposed method is applicable to both binary and multiclass classifications for the driving data of five drivers. In the case of motorway, the XGBoost classifier identifies each driver and delivers an accuracy of up to 96.18% in binary classification and an accuracy of 91.6% in multiclass classification. Moreover, in the case of an urban road, the SVM classifier achieves an accuracy of up to 95.07% in binary classification and accuracy of 89.06% in multiclass classification. The proposed method provides a context for a better understanding of the association between driver behavior, which is an in-vehicle event, and mechanical reactions. Our results shall help researchers to broaden the understanding of driver identification using in-vehicle data.
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