Key result
A driver fatigue recognition model was constructed using EEG signals, DPCA clustering algorithm, and a convolutional neural network based on fine operation and emergency response capabilities.
Why the study?
Driver fatigue directly affects the safety factor of compact vehicle driving, and detecting fatigue is important for safe driving and timely mental state adjustment.
EEG signals combined with DPCA clustering and CNN can be used to construct a driver fatigue recognition model.
May support EEG-based fatigue monitoring in drivers; leaves open real-world validation before clinical use.
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).
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Nian et al. (2021) studied Driver fatigue. EEG signal-based driver fatigue recognition model using DPCA clustering and CNN was evaluated on Classification of driver fatigue. A driver fatigue recognition model was constructed using EEG signals, DPCA clustering algorithm, and a convolutional neural network based on fine operation and emergency response capabilities.
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