In-vehicle information system as a driver's secondary activity: Case study

A robust methodology for detecting and evaluating driver distraction induced by in-vehicle information system using artificial neural network and fuzzy logic is introduced in this paper. An artificial neural network is used to predict driver's performance on a specific road segment. The predict...

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Bibliographic Details
Main Authors: Aksjonov, Andrei, Nedoma, Pavel, Vodovozov, Valery, Petlenkov, Eduard
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Fuzzy logic
machine learning
prediction methods
Roads
Task analysis
Vehicle dynamics
vehicle safety
Vehicles
Wheels
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Summary:A robust methodology for detecting and evaluating driver distraction induced by in-vehicle information system using artificial neural network and fuzzy logic is introduced in this paper. An artificial neural network is used to predict driver's performance on a specific road segment. The predicted performance-based measures are compared to the driving with secondary task accomplishment. Fuzzy logic is applied to fuse the variables into a single output, which constitutes a level of driver distraction in percentage. The technique was tested on a vehicle simulator by ten drivers that exploited in-vehicle information system as a secondary activity. The driver-in-the-loop experiment outcomes are discussed.
ISSN:1943-6580
DOI:10.1109/AMC.2019.8371148