Abstract
This article presents a driver fatigue recognition model based on a BP neural network that was developed to identify a driver's fatigue level both subjectively and precisely. Fatigue level was classified into the following four levels: alert, mild fatigue, deep fatigue, and drowsiness. Alpha rhythms, beta rhythms, delta rhythms, the mean of heart rate, and standard deviation of R-R intervals were selected as input parameters of the BP neural network and the driver's fatigue level was set as the output parameter. Part of the experimental data, collected from the JiangYan freeway, was used to train the BP neural network model, and the parameters of the model were calibrated. Then residual data was used to test the effectiveness of the model. Results showed that the model can recognize driver fatigue levels with high precision.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
