###### Abstract  
The detection of drowsiness while driving plays a vital role in ensuring road safety. Existing detection methods need to reduce external interference and sensor intrusiveness, and their algorithms must be modified to improve accuracy, stability, and timeliness. In order to realize fast and accurate driving drowsiness detection using physiological data that can be collected non-intrusively, a hybrid model with principal component analysis and artificial neural networks was proposed in this study. Principal component analysis was used to remove the noise and redundant information from the original data, and artificial neural networks were used to classify the processed data. Three other models were designed for comparison, including a hybrid model with principal component analysis and classic machine learning algorithms, a single model with artificial neural networks, and a single model with classic machine learning algorithms. The results indicated that the average accuracy of the proposed model exceeded 97%, the average training time was lower than 0.3 s, and the average standard deviation of the proposed model's accuracy was 0.7%, indicating that the model could detect driving drowsiness more accurately and quickly than the comparison models while ensuring stability. Thus, principal component analysis can help to improve the accuracy of driving drowsiness detection. This method can be applied to active warning systems (AWS) in intelligent vehicles in the future. road safety; driving drowsiness detection; non-intrusive physiological data; principal component analysis; artificial neural networks +
Footnote †: journal: Applied Sciences  
conditions. In addition, drivers cannot wear glasses, masks, and other objects that block the face, which is unrealistic for actual driving [7,8]. The accuracy of most driving drowsiness detection methods based on driving behavior is around 75%. With these methods, the accuracy is affected by the external environment, and differences in driving habits significantly reduce the robustness of the model [9]. Physiological signals can directly reflect the driver's state with a minimal time delay and they are not easily affected by the external environment. For this reason, many studies have applied physiological signals for the detection of drowsiness. In previous studies, electroencephalogram (EEG) and electrocardiogram (ECG) signals have been the most commonly used physiological signals for drowsiness detection. EEG, which is called the \"golden standard\" of drowsiness detection, can intuitively and effectively reflect the electrical activity information of the brain; thus, it has a wide range of applications in assessing the alertness of the brain [10, 11, 12]. ECG can be used to calculate heart rate variability (HRV), which refers to the tiny variations in successive heartbeat intervals. HRV is sensitive to drowsiness and is not easily affected by external environments [13]. However, both EEG and ECG are too intrusive to allow for the collection of data during actual driving [14]. Despite their efficiency, it is challenging to apply EEG and ECG for drowsiness detection in real-world driving scenarios. Several studies have indicated that electrodermal activity (EDA), respiration (RESP), and photoplethysmography (PPG) signals can also reflect drivers' drowsiness with small and light devices, which present little interference to drivers [15, 16, 17, 18, 19]. Electrodermal activity (EDA) refers to the small changes in the skin's electrical activity [20,21]. [PERSON], et al. [16] found that EDA signals became unstable when the participants became drowsy. Respiration (RESP) signal values show changes in the chest when a human breathes. [PERSON] et al. [15] found that drowsy drivers had a longer respiration period and a larger respiration amplitude than those who were awake. In addition, awake drivers hardly yawned, while drowsy drivers yawned frequently. However, neither [PERSON], et al. [16] nor [PERSON], et al.