Driver Fatigue Warning Based on Medical Physiological Signal Monitoring for Transportation Cyber-Physical Systems

Abstract

Driver fatigue detection is a critical challenge in Transportation Cyber-Physical Systems (T-CPS), where existing methods often face significant limitations. Conventional approaches typically struggle with issues such as limited accuracy, slow convergence, and high computational costs. These methods often fail to capture the complex temporal and spatial patterns inherent in multimodal physiological signals, such as EEG and EOG, leading to suboptimal performance in real-world scenarios. To address these challenges, we propose a novel approach that leverages Depthwise Separable Convolutional Neural Networks (DSCNNs) for driver fatigue detection. Our method integrates electroencephalogram (EEG) and electrooculogram (EOG) signals through an innovative feature extraction and fusion process, enhancing the system’s ability to detect fatigue with high accuracy and efficiency. The DSCNN model is designed to overcome the limitations of traditional methods by utilizing depthwise separable convolutions, which reduce computational complexity while maintaining robust performance. We validate our approach using the SEED-VIG dataset and the NTHU drowsy driver dataset, which encompasses a range of driving conditions. The DSCNN model outperforms conventional models in both accuracy and computational efficiency. Specifically, DSCNN achieves the highest F1 score and the lowest time per epoch, making it highly suitable for real-time applications in T-CPS. This advancement represents a significant improvement in driver safety by providing more timely and reliable fatigue warnings, thus advancing the capabilities of intelligent transportation systems.