Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection. And by harnessing the power of a Transformer architecture, sophisticated and long-term temporal features are adeptly extracted from video sequences, paving the way for more nuanced and accurate fatigue analysis. The proposed CT-Net (CLIP-Transformer Network) achieves an AUC (Area Under the Curve) of 0.892, a 36% accuracy improvement over the prevalent CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) end-to-end model, reaching state-of-the-art performance. Experiments show that the CLIP pre-trained model more accurately extracts facial and behavioral features from driver video frames, improving the model’s AUC by 7% over the ImageNet-based pre-trained model. Moreover, compared with LSTM, the Transformer more flexibly captures long-term dependencies among temporal features, further enhancing the model’s AUC by 4%.

The rapid development of urbanization has led to the gradual emergence of more traffic problems emerging; traffic incidents continue to increase in number and scope, and safety has become a serious issue. With the steady increase of vehicles and the contradiction between the supply and demand of road transportation, efficient solutions to traffic congestion and safety are paramount; intelligent transportation systems are the key to improving urban traffic. Based on the intelligent transportation decision system, this paper briefly discusses how to use business intelligence to conduct big data mining and analysis using data warehouses and online analytical processing technology to realize the automation of data reports, analyze the causes of traffic accidents, make scientific predictions of possible accidents, improve road capacity, and enhance the intelligence and informatization of the transportation decision system.