## 9 Conclusions and Future Work  
In this paper, we proposed ReSVM, a residual neural network with an SVM classifier, for detecting various types of drivers' distractions, including texting, operating the radio, drinking, talking on the phone, combing, and applying makeup. We compared ReSVM with seven state-of-the-art approaches using four publicly available datasets. The results showed that ReSVM outperformed the other approaches and achieved a classification accuracy as high as 95.5%. ReSVM, obtained by replacing the ResNet-50 classifier with an SVM, showed a percentage improvement of [7.3, 3.31, 5.83, and 14.54] on SFDDD, DrivFace, BU, and FT-UMT, respectively, as compared to ResNet-50. This significant percentage increase in the BU dataset is due to that fact that SVM performs well on missing values and dim light datasets. In future, we plan to explore additional features that can be useful for detecting distraction. Car motion can be an important indicator. For instance, a car swerving between lanes could imply distraction or driving under the influence of alcohol. Driver emotions, such as extreme anger, which have the potential to adversely affect the driver's ability to drive safely, could be another strong indicator for distraction. Jitter limbs and other tics could also be useful for our purpose as they could imply tiredness or health issues. In this paper, we performed the classification based only on the spatial features, i.e., on images. It is important to note that very short duration events, e.g., glancing down for a fraction of a second, might not be problematic, and hence should not be classified as distraction. These temporal aspects of distraction will be explored in our future work. We also plan to develop approaches for monitoring unsafe driving behavior which may help prevent accidents, as well as assist law enforcement agencies. Among other things, this could include traffic signal and rule violations, speeding, tailgating, and sudden acceleration/deceleration for no apparent reason. Eventually, we also plan to go live \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Class** & **C0** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** \\ \hline C0 & 0.00 & 0.50 & 1.50 & 2.50 & 3.50 & 3.50 & 3.50 & 3.50 & 3.50 & 3.50 \\ C1 & 0.50 & 1.50 & 2.50 & 3.50 & 4.00 & 4.00 & 4.00 & 4.00 & 4.00 & 4.00 \\ C2 & 1.50 & 2.50 & 0.50 & 2.75 & 3.60 & 3.90 & 3.80 & 3.80 & 2.50 & 2.90 \\ C3 & 2.50 & 3.50 & 2.75 & 0.50 & 1.50 & 1.90 & 1.95 & 2.50 & 2.80 & 3.20 \\ C4 & 3.50 & 4.00 & 3.60 & 1.50 & 0.50 & 0.50 & 1.90 & 2.10 & 2.30 & 3.10 \\ C5 & 3.50 & 4.00 & 3.90 & 1.90 & 0.50 & 0.50 & 2.50 & 2.40 & 3.10 & 3.30 \\ C6 & 3.50 & 4.00 & 3.80 & 1.95 & 1.90 & 2.50 & 0.50 & 0.50 & 0.50 \\ C7 & 3.50 & 4.00 & 3.80 & 2.50 & 2.10 & 2.40 & 0.50 & 0.50 & 0.50 & 0.50 \\ C8 & 3.50 & 4.00 & 2.50 & 2.80 & 2.30 & 3.10 & 0.50 & 0.50 & 0.50 & 0.50 \\ C9 & 3.50 & 4.00 & 2.90 & 3.20 & 3.10 & 3.30 & 0.50 & 0.50 & 0.50 & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of within-class (average distance) and between-class (average linkage) distances for SFDDD dataset. and develop a distraction detection and alerting system in cars and evaluate its actual performance on roads. The addition of large data repositories for deep architectures will also be our future goal. Conceptualization, S.F.A., O.T. and A.M.; Methodology, S.F.A., O.T. and A.Z.K. ; Software, T.A. and S.F.A. ; Validation, T.A., S.F.A. and A.Z.K. ; Formal Analysis, T.A. and O.T. ; Investigation, S.F.A. ; Resources, T.A., M.A.M. and S.F.A. ; Data Curation, S.F.A., M.A.M. and A.M.; Writing--Original Draft Preparation, S.F.A. and O.T. ; Writing--Review and Editing, A.Z.K. and O.T. ; Visualization, S.F.A. and O.T. ; Supervision, A.Z.K. and A.M.; Project Administration, S.F.A., M.A.M. and M.J.A. ; Funding Acquisition, M.J.A. and M.A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.