Can EEG Measurements be Used to Estimate the Performance of Taking over Control from an Autonomous Vehicle for Different Levels of Distracted Driving? An Explorative Study

Loading...
Thumbnail Image

Date

Authors

Miltenburg, M.P.G. van
Lemmers, D.J.A.
Tinga, A.M.
Christoph, M.W.T.
Zon, G.D.R.

Journal Title

Journal ISSN

Volume Title

Publisher

ACM Digital Library

License Holder

© 2022 by the authors

Licence Type

Sponsor

This research was funded from the EU’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 814735. This paper does not necessarily reflect the views of the European Commission.

Abstract

Driver distraction is a concern for traffic safety. Most research has been focused on validating or quantifying the relationship between eyes-off-road metrics and driving performance without specifically addressing cognitive aspects of distracted driving. The current study explores to what extent electroencephalogram data is a good predictor of how successful a distracted driver will be able to take over control from an autonomous vehicle. Participants were driving a simulated car while being exposed to varying levels of distraction. During the ride at several moments the participants were warned to take over control, after which the control was transferred. Sometimes after taking over the control an immediate break action of the drivers was expected. It turned out that electroencephalogram based data is able to indicate to what extent participants are distracted. However, electroencephalogram based data is not able to estimate driving performance during take over control.

Description

Keywords

Citation

Van Miltenburg, M. M. P. G., Lemmers, D. J. A., Tinga, A., Christoph, M., & Zon, R. (2022). Can EEG Measurements be Used to Estimate the Performance of Taking over Control from an Autonomous Vehicle for Different Levels of Distracted Driving? An Explorative Study. In Adjunct Proceedings - 14th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications, AutomotiveUI 2022 (pp. 20-24). Association for Computing Machinery. https://doi.org/10.1145/3544999.3552324

Endorsement

Review

Supplemented By

Referenced By