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

Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa
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Author to whom correspondence should be addressed.
The authors contributed equally to this work.
Academic Editor: Felipe Jimenez
Sensors 2015, 15(12), 30653-30682; https://doi.org/10.3390/s151229822
Received: 14 July 2015 / Revised: 11 November 2015 / Accepted: 16 November 2015 / Published: 4 December 2015
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: driving style; driver behaviour; artificial intelligence; machine learning; driver safety; road accident; driver identification driving style; driver behaviour; artificial intelligence; machine learning; driver safety; road accident; driver identification
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MDPI and ACS Style

Meiring, G.A.M.; Myburgh, H.C. A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms. Sensors 2015, 15, 30653-30682.

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