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

Gys Meiring · Hermanus Carel Myburgh

273 citations0/0 files storedSensors

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Human-Automation Interaction and Safety Traffic and Road Safety Autonomous Vehicle Technology and Safety

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.

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References

1965Fuzzy sets 1996Fuzzy sets 2005Detecting Stress During Real-World Driving Tasks Using Physiological Sensors 2011Driver Inattention Monitoring System for Intelligent Vehicles: A Review 2013Driver classification and driving style recognition using inertial sensors 2002Graphical models for driver behavior recognition in a SmartCar

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