Review on Recognition of Unsatisfactory Driving Status Based on Electroencephalogram
Driver condition,as a crucial component of the"human-vehicle-road-environment"transport system,significantly affects driving behavior.Electroencephalogram(EEG)serves as a direct indicator of brain activity and can accurately reflect a driver's current state during driving.This paper begins by outlining the inherent relationship between EEG and adverse driving conditions,such as distraction,fatigue,and emotion based on the literature.Subsequently,key aspects of the test environment,data processing,and analysis methods employed in EEG research are summarized.The summary reveals that the essence of most studies can be interpreted as an exploration of the qualitative and quantitative relationships between various driver states and EEG.EEG data is collected through the simulated driving by volunteers,the EEG characteristic values are extracted by linear or nonlinear analysis methods,and then the driver's state is identified by mathematical models or neural network models.Furthermore,to enhance the accuracy of recognition models,research on multi-source information fusion based on EEG in scenarios like unsatisfactory driving state has gradually increased.The application of EEG in driving state recognition system is progressively moving towards commercialization.This indicates that current driving state recognition algorithms based on EEG possess promising safety application potential and prospects.Nonetheless,there remains significant room for improvement in areas,such as EEG feature extraction,real-time processing,and recognition accuracy across various driver states.
traffic engineeringelectroencephalogramreviewdistraction drivingfatigue driv-ingemotion drivingmulti-source information fusiontraffic safety
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