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 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.
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- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14248220
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Supplemental Notes:
- © 2015 Gys Albertus Marthinus Meiring and Hermanus Carel Myburgh.
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Authors:
- Meiring, Gys Albertus Marthinus
- Myburgh, Hermanus Carel
- Publication Date: 2015
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; Maps; Photos; References;
- Pagination: pp 30653-30682
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Serial:
- Sensors
- Volume: 15
- Issue Number: 12
- Publisher: MDPI AG
- ISSN: 1424-8220
- Serial URL: http://www.mdpi.com/journal/sensors
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Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Algorithms; Artificial intelligence; Behavior; Driver support systems; Drivers; Fuzzy logic; Human machine systems; State of the art; Vector analysis
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors;
Filing Info
- Accession Number: 01603791
- Record Type: Publication
- Files: TRIS
- Created Date: Jun 29 2016 1:25PM