Predicting the EEG Level of a Driver Based on Driving Information

Driving is an essential activity in today's busy and complex society, and it demands physical and mental abilities, collectively known as a driving workload. For safe and comfortable driving, it would be useful to detect when drivers are being overloaded. Analyzing driver's workload using...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2019-04, Vol.20 (4), p.1215-1225
Main Authors: Kim, Hyun Suk, Yoon, Daesub, Shin, Hyun Soon, Park, Cheong Hee
Format: Article
Language:English
Subjects:
Brain modeling
Cognitive workload
driver behavior
driver workload
electroencephalograph
Electroencephalography
Feature extraction
Lane changing
Measuring instruments
Physiology
Predictive models
Roads
support vector machine
Support vector machines
Task analysis
Test procedures
Vehicles
Workload
Workloads
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Summary:Driving is an essential activity in today's busy and complex society, and it demands physical and mental abilities, collectively known as a driving workload. For safe and comfortable driving, it would be useful to detect when drivers are being overloaded. Analyzing driver's workload using an electroencephalograph (EEG) is useful for this purpose. However, it is very inconvenient to obtain an EEG during actual driving, since the measuring device needs to be attached to the driver. In this paper, we develop a model to predict the driver's EEG level utilizing basic information obtained while the vehicle is being driven. We divided the EEG values into two classes, "normal" and "overload", and extracted useful features from the vehicle driving information, such as engine RPM, vehicle speed, lane changes, and turns. A classification model using a support vector machine was built to predict normal and overload states during actual driving. We evaluated the performance of the proposed method using field-of-test data collected when driving on actual roads, and suggest directions for future research based on an analysis of the experimental results.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2848300