A Novel Driver Performance Model Based on Machine Learning

Models of road vehicle driver behaviour are widely used in several disciplines, like driver distraction and autonomous driving. In this paper, a novel driver performance model, which is unique for every driver, is introduced. The driver is modelled with machine learning algorithms, namely artificial...

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Bibliographic Details
Published in:IFAC-PapersOnLine 2018, Vol.51 (9), p.267-272
Main Authors: Aksjonov, Andrei, Nedoma, Pavel, Vodovozov, Valery, Petlenkov, Eduard, Herrmann, Martin
Format: Article
Language:English
Subjects:
adaptation in autonomous vehicles
control
Human factors in vehicular system
Learning
Machine learning for environmental applications
Neural fuzzy modelling
Neural networks
Safety
Vehicle dynamic systems
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Summary:Models of road vehicle driver behaviour are widely used in several disciplines, like driver distraction and autonomous driving. In this paper, a novel driver performance model, which is unique for every driver, is introduced. The driver is modelled with machine learning algorithms, namely artificial neural network and adaptive neuro-fuzzy inference system. Every model is trained and validated with the data collected during the real-time driver-in-the-loop experiment on a vehicle simulator for each driver separately. In total, 18 participants contributed to the experiment. Although the prediction accuracy of the models depends on the algorithm specifications, the artificial neural network was slightly more accurate in driver performance prediction comparing to the adaptive neuro-fuzzy inference system. The driver models may be used in detection of driver distraction induced by in-vehicle information system.
ISSN:2405-8963
2405-8963
DOI:10.1016/j.ifacol.2018.07.044