Citation

Lv C, Nian J, Xu Y, Song B. IEEE Trans. Intel. Transp. Syst. 2022; 23(10): 19753-19759.

Copyright

(Copyright © 2022, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2021.3119354

PMID

unavailable

Abstract

The driver's fatigue directly affects the safety factor of the compact vehicle driving in actual road. Mastering the driver's fatigue state plays an important role in the driver's safety driving and timely adjustment of mental state. In view of the particularity of the driving safety of the compact vehicle, this paper takes the driver's brain electricity (EEG) signal as the research object, and starts from the formulation of the experimental scheme, and based on the special training system in the simulation driving software. Two types of driving quality evaluation indicators: the fine operation ability and emergency response capability is formulated; after preprocessing and eigenvalue selection of EEG signals, DPCA clustering algorithm combined with driving quality is used to complete the classification of driver fatigue and the marking of EEG signal feature data set. Finally, the driver fatigue recognition model is initially constructed by using the labeled data set combined with the convolutional neural network (CNN).

Language: en

Keywords

Brain modeling; Clustering algorithms; convolutional neural network; DPCA; EEG; Electroencephalography; Fatigue; Fatigue recognition; Software; Vehicles; wavelet packet; Wavelet packets