## 4 Results
### Results of Distracted Driving Detection  
We divided our distracted driving front data set into a training set and a validation set, with 26,223 frames and 6553 frames, respectively. Table 5 shows the confusion matrix for the distracted driving front data set. We evaluated the CNN model, which was trained on data from 11 participants, and on the data from the one remaining participant. The overall training accuracy of the CNN model was 99.7%. The average accuracy, calculated as the number of correct frames divided by the total number of frames, was 91.6%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy** \\ \hline [PERSON] et al. [20] & 88.6\% \\ [PERSON] and [PERSON] [21] & 83.0\% \\ [PERSON] et al. [35] & 79.8\% \\ [PERSON] and [PERSON] [36] & 85.6\% \\ Our approach & 91.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of our approach with the previous methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Precision** & **Recall** & **F1 Score** & **Support** \\ \hline
0 & 0.89 & 1 & 0.94 & 8 \\ \(1\) & \(1\) & 0.67 & 0.8 & 3 \\ Accuracy & - & - & 0.91 & 11 \\ Macro average & 0.94 & 0.83 & 0.87 & 11 \\ Weighted average & 0.92 & 0.91 & 0.9 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results from the random forest algorithm and parameter settings.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fatigue Parameters** & **Importance** \\ \hline YawnFreq & 0.387 \\ Blink frequency & 0.149 \\ PERCLOS & 0.130 \\ GazeDir (left) & 0.101 \\ NodFreq & 0.077 \\ MCD & 0.058 \\ GazeDir (center) & 0.055 \\ GazeDir (right) & 0.042 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Importance of the fatigue parameters.  
To compare the distracted driving detection methods, we used the Kaggle distracted driving data set for training and validation. This driving data set was taken by a camera mounted in the car. The data set contained a total of 22,424 training images and 79,726 testing images. The distracted driving behaviors in the Kaggle data set [37] fell into 1 of 10 categories: \"safe driving\" (c0), \"texting-right hand\" (c1), \"talking on the phone-right hand\" (c2), \"texting-left hand\" (c3), \"talking on the phone-left hand\" (c4), \"operating the radio\" (c5), \"drink\" (c6), \"reaching behind\" (c7), \"hair and makeup\" (c8), and \"talk to passengers\" (c9). For each test image, our approach assigned a probability for each of the 10 categories. We compared the proposed distracted driving detection against several previous methods [27, 28, 38]. Table 6 shows the comparison of various distracted driving detection techniques. In the proposed approach, the training accuracy and log loss were 98.3% and 0.17, respectively. The accuracy and log loss of the validation set were 97.5% and 0.11, respectively. These results demonstrated the superiority of our approach over existing methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **c0** & **c1** & **c2** & **c3** & **c4** & **c5** & **c6** \\ \hline c0 & 1032 & 0 & 4 & 5 & 0 & 0 & 0 \\ c1 & 0 & 4996 & 1 & 0 & 0 & 0 & 0 \\ c2 & 0 & 0 & 4724 & 0 & 0 & 1 & 0 \\ c3 & 1 & 0 & 2 & 3559 & 0 & 7 & 0 \\ c4 & 9 & 0 & 1 & 0 & 4756 & 1 & 0 \\ c5 & 33 & 1 & 0 & 1 & 0 & 5176 & 0 \\ c6 & 0 & 12 & 0 & 5 & 0 & 0 & 1689 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Confusion matrix for the distracted driving front data set.  
Figure 6: Detection of distracted driving.  
Table 7 shows the confusion matrix for the Kaggle data set. The frame rates were approximately 140-170 and 40-70 fps for the PC and Jetson TX2, respectively. The results revealed that the proposed approach can achieve real-time detection on the Nvidia Jetson TX2 platform.