Fatigue driving detection model based on multi‐feature fusion and semi‐supervised active learning

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Fatigue driving is one of the main factors of traffic accidents and there are many research efforts focusing on fatigue driving detection. With the extensive use of on-board sensors, a huge number of unlabelled driving data can be easily collected, however, it is a costly and laborious work to annotate semantic labels for these data manually, posing some difficulties to detect fatigue driving with these data. In this work, the authors propose a novel fatigue driving detection model based on multi-feature fusion and semi-supervised active learning. In the authors' model, the steering features of the vehicle and the facial features of the driver are fused to improve the accuracy and stability of the model. Semi-supervised active learning algorithm allows us to make semantic labels for only a small number of data that can be propagated to the rest data, and help us establish an efficient fatigue driving detection model with automatic label propagation. Some experiments are conducted to validate their model, the results show that the accuracy is 86.25%, which proves the effectiveness of the fatigue driving detection model.