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.
G.A.M. Meiring performed this work as part of his Master’s degree in Computer
Engineering, under the supervision of H.C. Myburgh. This work is the combination of three research
assignments in the form of an exam assignment. Each assignment was thoroughly reviewed and graded by
H.C. Myburgh, who also provided detailed feedback, which G.A.M. Meiring incorporated in the final draft.
H.C. Myburgh also prepared this manuscript from the exam assignment submitted by G.A.M. Meiring.