Citation

Wang B, Wang J, Wang X, Chen L, Zhang H, Jiao C, Wang G, Feng K. Sensors (Basel) 2024; 24(13).

Copyright

(Copyright © 2024, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s24134392

PMID

39001171

PMCID

PMC11244404

Abstract

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

Language: en

Keywords

Humans; Accidents, Traffic; EEG; vehicle; *Automobile Driving; *Neural Networks, Computer; *Electroencephalography/methods; *Hypnosis/methods; drivers; road hypnosis; state identification