IDSRN: Interpretable Dynamic System Recurrent Network for Driver Fatigue Assessment

Driver fatigue is a critical factor contributing to traffic accidents. Therefore, real-time and accurate recognition of driver fatigue states holds significant importance. This paper proposes a novel driver fatigue detection method based on electroencephalogram (EEG) signals and an Interpretable Dynamic System Recurrent Network (IDSRN). The IDSRN integrates the temporal modeling capability of traditional Recurrent Neural Networks (RNNs) with the nonlinear function approximation advantages of polynomial networks, enabling effective extraction of nonlinear dynamic features from EEG signals. This study collected EEG data from drivers under varying fatigue states and constructed input vectors suitable for classification tasks through preprocessing and feature extraction. The extracted features were subsequently fed into the IDSRN model for training and testing, with comparative analyses conducted against traditional methods (e.g., SVM, CNN, and standard RNN). Experimental results demonstrate that the IDSRN outperforms other models in recognition accuracy (average: 92.3%), convergence speed, and robustness, significantly improving the efficacy of driver fatigue-state identification.