title: A Review of Intelligent Driving Style Analysis Systems and Related Artificial Intelligence Algorithms.
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.
authors.affiliation: Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa. gam.meiring@gmail.com.
authors.forename: Gys Albertus Marthinus
authors.initials: GA
authors.lastname: Meiring
authors.affiliation: Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Private Bag X20, Hatfield, Pretoria 0028, South Africa. herman.myburgh@up.ac.za.
authors.forename: Hermanus Carel
authors.initials: HC
authors.lastname: Myburgh
keywords: artificial intelligence; driver behaviour; driver identification; driver safety; driving style; machine learning; road accident