A hybrid GCN-LSTM model for driver drowsiness detection

Driver drowsiness detection is essential in the field of intelligent driving and should be solved timely. Although the application of convolutional neural networks has brought about great progress in this field, they do not perform well in complex driving scenarios due to their inability to extract comprehensive spatio-temporal information well. In this paper, a Hybrid model using Graph networks and Long short-term memory networks for Drowsiness Detection (HGLDD) is proposed for the first time to fuse the driver’s facial depth information and head posture information together with eye and mouth information from facial landmark sequence. The model extracts spatio-temporal depth features and determines whether or not the driver is in a drowsy state. On the Nthu-DDD dataset, the proposed model ultimately achieves an average accuracy of 98.01%, demonstrating its applicability to drowsiness detection tasks in real driving scenarios.