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.
A Driver Fatigue Recognition Model Based on BP Neural Network
11th International Conference of Chinese Transportation Professionals (ICCTP) ; 2011 ; Nanjing, China
ICCTP 2011 ; 2072-2079
2011-07-26
Conference paper
Electronic Resource
English
Structuring neural network driver model
Automotive engineering | 2000
|Evaluation of driver fatigue with multi-indicators based on artificial neural network
IET | 2016
|Evaluation of driver fatigue with multi‐indicators based on artificial neural network
Wiley | 2016
|