## 5 Discussion  
The proposed methodology is able to demonstrate the effectiveness of deep learning in the smart connected cars. The CNN of this work is able to detect distracted drivers while performing dangerous tasks such as texting and driving. According to the results obtained, the CNN presents a generalized behavior since the evaluation using the test dataset presents very similar values to the obtained using the train dataset, even when  
Figure 10: CNN Confusion matrix for the blind dataset. Figure 9: CNN Confusion matrix for the test dataset. the images were not seen by the CNN in the training stage, suggesting the high degree of detection once the system is fully trained. On the other hand, in order to validate the CNN performance in a more realistic scenario, a blind dataset was used, where the data contained only unseen samples from different subjects, vehicles and cellphones, achieving an accuracy of 0.80, sensitivity and specificity of 0.97 and 0.75, respectively, and kappa value of 0.57. As it is evident, the CNN performance presents a decrease in its values due to the complete change of the environmental conditions; however, the CNN is still able to detect the texting and driving action with statistically significant performance. From the confusion matrices for training and testing presented in Figures 8 and 9, it is possible to observe that the performance of the CNN for the training and testing stages is very similar, with less than 1% of a difference in the false positive (FP) and, when the CNN is compared with the blind data set presented in Figure 10, an increase of 15.5% in the false negative (FN) with respect to the original testing data set is obtained; nevertheless, the FP, with a value of 0.06% in the blind test, remained within the original training values; therefore, even with different conditions, the CNN is able to precisely detect the \"texting and driving\" condition with a low rate of FN and FP. From the validation stage, Figure 7 shows that the performance of the training and testing is also very similar, obtaining both AUC values higher than 0.88, being statistically significant, as well as the value obtained with the blind dataset, which is higher than 0.86, demonstrating again that the CNN is capable of detecting the \"texting and driving\" task in different conditions, suggesting the robustness of the CNN model. In Table 2, a comparison of the presented research against two similar approaches is shown. These approaches are \"SmartSence\" and \"TEXIVE\", where, from the SmartSence, no performance is disclosed and, from the TEXIVE, the authors present an accuracy of 87.18%; however, even when TEXIVE presents statistically significant performance, it is based on the assumption that the subject always carries the phone in the same position (i.e., left pocket) prior to entering the car, and it is placed or held in the same place inside the car. [PERSON] et al. [18] focused instead on the detection of driving and did not detect distracted drivers. Lastly, [PERSON] et al. [19] detected aggressive driver detection; however, the author did not disclose any performance regarding distracted driving. From this table, it is possible to observe that the methodology presented in this work exceeded any other similar approaches; in addition, it is not necessary to force the driver to carry the phone in any way or special place. The present study limits the system to a green box delimited by return on investment, shown in Figure 2b; this restriction was selected as the most common place to hold the phone while texting, since, in order to write a text, the subject should see the screen of the phone; therefore, holding the phone outside of the ROI and texting represent only a small fraction of the total cases; nevertheless, future research will focus on addressing such scenarios. As mentioned in the related work in Section 2, several approaches have been proposed to solve the problem of distracted drivers [13; 15; 16; 17; 18]. Most of