Driver Identification Based on Wavelet Transform Using Driving Patterns

Byung Il Kwak, Mee Lan Han, Huy Kang Kim

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


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.

Original languageEnglish
Article number9108547
Pages (from-to)2400-2410
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number4
Publication statusPublished - 2021 Apr

Bibliographical note

Funding Information:
This work was supported by Samsung Research Funding & Incubation Center for Future Technology under Project SRFC-TB1403-51.

Publisher Copyright:
© 2005-2012 IEEE.


  • Driver identification
  • driving-pattern analysis
  • machine learning
  • wavelet transform

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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