A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms
Identifieur interne : 000102 ( Pmc/Checkpoint ); précédent : 000101; suivant : 000103A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms
Auteurs : Gys Albertus Marthinus Meiring ; Hermanus Carel MyburghSource :
- Sensors (Basel, Switzerland) [ 1424-8220 ] ; 2015.
Abstract
In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.
Url:
DOI: 10.3390/s151229822
PubMed: 26690164
PubMed Central: 4721742
Affiliations:
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PMC:4721742Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.</p>
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<pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Sensors (Basel)</journal-id>
<journal-id journal-id-type="iso-abbrev">Sensors (Basel)</journal-id>
<journal-id journal-id-type="publisher-id">sensors</journal-id>
<journal-title-group><journal-title>Sensors (Basel, Switzerland)</journal-title>
</journal-title-group>
<issn pub-type="epub">1424-8220</issn>
<publisher><publisher-name>MDPI</publisher-name>
</publisher>
</journal-meta>
<article-meta><article-id pub-id-type="pmid">26690164</article-id>
<article-id pub-id-type="pmc">4721742</article-id>
<article-id pub-id-type="doi">10.3390/s151229822</article-id>
<article-id pub-id-type="publisher-id">sensors-15-29822</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
</subj-group>
</article-categories>
<title-group><article-title>A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Meiring</surname>
<given-names>Gys Albertus Marthinus</given-names>
</name>
<xref ref-type="author-notes" rid="fn1-sensors-15-29822">†</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Myburgh</surname>
<given-names>Hermanus Carel</given-names>
</name>
<xref rid="c1-sensors-15-29822" ref-type="corresp">*</xref>
<xref ref-type="author-notes" rid="fn1-sensors-15-29822">†</xref>
</contrib>
</contrib-group>
<contrib-group><contrib contrib-type="editor"><name><surname>Jimenez</surname>
<given-names>Felipe</given-names>
</name>
<role>Academic Editor</role>
</contrib>
</contrib-group>
<aff id="af1-sensors-15-29822">Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa;<email>gam.meiring@gmail.com</email>
</aff>
<author-notes><corresp id="c1-sensors-15-29822"><label>*</label>
Correspondence: <email>herman.myburgh@up.ac.za</email>
; Tel.: +27-124-204-540; Fax: +27-123-265-000</corresp>
<fn id="fn1-sensors-15-29822"><label>†</label>
<p>The authors contributed equally to this work.</p>
</fn>
</author-notes>
<pub-date pub-type="epub"><day>04</day>
<month>12</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="collection"><month>12</month>
<year>2015</year>
</pub-date>
<volume>15</volume>
<issue>12</issue>
<fpage>30653</fpage>
<lpage>30682</lpage>
<history><date date-type="received"><day>14</day>
<month>7</month>
<year>2015</year>
</date>
<date date-type="accepted"><day>16</day>
<month>11</month>
<year>2015</year>
</date>
</history>
<permissions><copyright-statement>© 2015 by the authors; licensee MDPI, Basel, Switzerland.</copyright-statement>
<copyright-year>2015</copyright-year>
<license><license-p><pmc-comment>CREATIVE COMMONS</pmc-comment>
This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
).</license-p>
</license>
</permissions>
<abstract><p>In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning and artificial intelligence algorithms utilised in current driver behaviour and driving style analysis systems. This review therefore serves as a trove of information, and will inform the specialist and the student regarding the current state of the art in driver style analysis systems, the application of these systems and the underlying artificial intelligence algorithms applied to these applications. The aim of the investigation is to evaluate the possibilities for unique driver identification utilizing the approaches identified in other driver behaviour studies. It was found that Fuzzy Logic inference systems, Hidden Markov Models and Support Vector Machines consist of promising capabilities to address unique driver identification algorithms if model complexity can be reduced.</p>
</abstract>
<kwd-group><kwd>driving style</kwd>
<kwd>driver behaviour</kwd>
<kwd>artificial intelligence</kwd>
<kwd>machine learning</kwd>
<kwd>driver safety</kwd>
<kwd>road accident</kwd>
<kwd>driver identification</kwd>
</kwd-group>
</article-meta>
</front>
</pmc>
<affiliations><list></list>
<tree><noCountry><name sortKey="Meiring, Gys Albertus Marthinus" sort="Meiring, Gys Albertus Marthinus" uniqKey="Meiring G" first="Gys Albertus Marthinus" last="Meiring">Gys Albertus Marthinus Meiring</name>
<name sortKey="Myburgh, Hermanus Carel" sort="Myburgh, Hermanus Carel" uniqKey="Myburgh H" first="Hermanus Carel" last="Myburgh">Hermanus Carel Myburgh</name>
</noCountry>
</tree>
</affiliations>
</record>
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