Driver Identification Based on Wavelet Transform Using Driving Patterns
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwak, Byung Il | - |
dc.contributor.author | Han, Mee Lan | - |
dc.contributor.author | Kim, Huy Kang | - |
dc.date.accessioned | 2021-08-30T02:46:11Z | - |
dc.date.available | 2021-08-30T02:46:11Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2021-04 | - |
dc.identifier.issn | 1551-3203 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/49436 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Driver Identification Based on Wavelet Transform Using Driving Patterns | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwak, Byung Il | - |
dc.contributor.affiliatedAuthor | Han, Mee Lan | - |
dc.contributor.affiliatedAuthor | Kim, Huy Kang | - |
dc.identifier.doi | 10.1109/TII.2020.2999911 | - |
dc.identifier.scopusid | 2-s2.0-85099516152 | - |
dc.identifier.wosid | 000607814600011 | - |
dc.identifier.bibliographicCitation | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, v.17, no.4, pp.2400 - 2410 | - |
dc.relation.isPartOf | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.citation.title | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | - |
dc.citation.volume | 17 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 2400 | - |
dc.citation.endPage | 2410 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Industrial | - |
dc.subject.keywordAuthor | Driver identification | - |
dc.subject.keywordAuthor | driving-pattern analysis | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | wavelet transform | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.