## 4 Results  
### Drowsiness Detection  
Table 7 shows the average accuracies, drowsiness recalls, drowsiness precisions, AUCs, and training times of the models; the best performance of each evaluation index for the hybrid and single models is bolded. The average accuracies, drowsiness recalls, and drowsiness precisions of the training results from all models were above 90%, and the average AUCs of most of the models were above 0.9. Different models showed different average training times. The average training time of PCA-SVM and SVM was about one minute, while the training times of other models were mainly within one second. Additional information can be found in Appendix A, which shows the results of drowsiness detection. The hybrid model utilizing principal component analysis and artificial neural networks obtained the highest accuracy, drowsiness recall, drowsiness precision, and AUC, while guaranteeing timeliness. The average accuracy of the hybrid model (PCA-CFNN) was 97.9%, which was up to 4.9% higher than the comparison models (KNN). The training time of PCA-CFNN was 0.231 s, up to 99% shorter than the comparison models (SVM). Although the training time was slightly longer than that for PCA-KNN (0.124 s) and KNN (0.116 s), it still satisfied the needs for application in real driving scenarios. The artificial neural networks performed better than the classic machine learning algorithms. For hybrid models, the average accuracies, drowsiness precisions, and AUCs of the artificial neural networks (PCA-BPNN and PCA-CFNN) were higher than classic machine learning algorithms (PCA-SVM and PCA-KNN). The drowsiness recalls of the artificial neural networks (PCA-BPNN and PCA-CFNN) were higher than that of PCA-KNN but lower than that of PCA-SVM. The single models had similar performance. The average accuracies, drowsiness precisions, and AUCs of the artificial neural networks (BPNN and \begin{table}
\begin{tabular}{c c} \hline
**Index** & **Description** \\ \hline Accuracy (\%) & Percentage of physiological state accurately detected. \\ Drowsiness recall (\%) & Percentage of drowsiness accurately detected. \\ Drowsiness precision (\%) & Percentage of the precise drowsiness output \\ AUC & Area under the ROC curve; reflects the model’s ability to classify positive and negative examples. \\ Training time (seconds) & Time required for model training. \\ \hline \end{tabular}
\end{table} 
Table 6: Evaluation indexes of numerical experiment. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Models**} & \multicolumn{4}{c}{**Hybrid Models**} & \multicolumn{4}{c}{**Single Models**} \\ \cline{2-9}  & \multicolumn{2}{c}{\begin{tabular}{c} **Hybrid Model of Principal** \\ **Component Analysis** \\ **and ANN** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Hybrid Model of Principal** \\ **Component Analysis** \\ **and CMA** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Single Model of** \\ **ANN** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Single Model of** \\ **CMLA** \\ \end{tabular} } \\ \cline{2-9}  & **PCA-BPNN** & **PCA-CFNN** & **PCA-SVM** & **PCA-KNN** & **BPNN** & **CFNN** & **SVM** & **KNN** \\ \hline Accuracy (\%) & 97.2 & 97.9 & 94.8 & 94.6 & 96.1 & 96.1 & 93.3 & 93.0 \\ \hline Drowsiness recall (\%) & 98.2 & 98.5 & 98.4 & 97.1 & 97.2 & 97.3 & 97.3 & 95.8 \\ \hline Drowsiness precision (\%) & 97.5 & 98.1 & 94.3 & 94.8 & 96.6 & 96.4 & 92.8 & 93.6 \\ \hline AUC & **0.973** & **0.973** & 0.914 & 0.920 & 0.970 & 0.970 & 0.892 & 0.900 \\ \hline Training time (seconds) & 0.182 & 0.231 & 50.719 & **0.107** & 0.441 & 0.982 & 64.963 & 0.115 \\ \hline \hline \end{tabular}  
* ANN—artificial neural networks; CMA—classic machine learning algorithms.  
\end{table} 
Table 7: The average accuracies, drowsiness recalls, drowsiness precisions, AUCs, and training times of the models in the drowsiness detection stage. CFNN) were higher than those of the classic machine learning algorithms (SVM and KNN).