A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms

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 a...

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Published inSensors (Basel, Switzerland) Vol. 15; no. 12; pp. 30653 - 30682
Main Authors Meiring, Gys, Myburgh, Hermanus
Format Journal Article
LanguageEnglish
Published Switzerland MDPI 04.12.2015
MDPI AG
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Online AccessGet full text
ISSN1424-8220
1424-8220
DOI10.3390/s151229822

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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.
AbstractList 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.
Author Meiring, Gys
Myburgh, Hermanus
AuthorAffiliation Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa; gam.meiring@gmail.com
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  surname: Myburgh
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/26690164$$D View this record in MEDLINE/PubMed
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Issue 12
Keywords driver identification
driving style
driver behaviour
driver safety
road accident
machine learning
artificial intelligence
Language English
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Snippet In this paper the various driving style analysis solutions are investigated. An in-depth investigation is performed to identify the relevant machine learning...
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StartPage 30653
SubjectTerms artificial intelligence
driver behaviour
driver identification
driver safety
driving style
machine learning
road accident
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Title A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms
URI https://www.ncbi.nlm.nih.gov/pubmed/26690164
https://www.proquest.com/docview/1751485374
https://pubmed.ncbi.nlm.nih.gov/PMC4721742
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