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

• Published in: Sensors (Basel, Switzerland) Vol. 15; no. 12; pp. 30653 - 30682
• Main Authors: Meiring, Gys, Myburgh, Hermanus
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 04.12.2015; MDPI AG
• Subjects: artificial intelligence; driver behaviour; driver identification; driver safety; driving style; machine learning; road accident; driver identification; driving style; driver behaviour; driver safety; road accident; machine learning; artificial intelligence
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s151229822

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.