## 5 Discussion
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Research Objective** & **Description** & **Performance** \\ \hline Our Approach & Distracted Drivers (Texting and driving) & Computer vision \& Convolutional Neural Network & 92.8\% Accuracy \\ \hline The SmartSense [12] & Distracted Drivers through head and eye movements & Computer vision \& Telematics & Non Disclosed \\ \hline TEXIVE [17] & Distracted Drivers (Texting and driving) & Phone based, Inertial Sensors, No Crossvalidation & 87.18\% Accuracy \\ \hline [PERSON] et al. [18] & Passanger or Driver Detection & Smartphone sensors, SVM classifier & 85\% Accuracy \\ \hline [PERSON] et al. [19] & Aggressive driver detection & Smartphone, K-means to detect aggressive driving & Non Disclosed \\ \hline \hline \end{tabular} SVM = Support-vector machine.  
\end{table} 
Table 2: Performance comparison of related work. the approaches are based on applications; therefore, it does not require any modification of hardware, while our approach uses fixed sensors for vehicles, while we can think that this implementation will have a higher cost; the CNN used in this investigation can export to low-cost computers like Raspberry Pi 3 and can even be incorporated into an Android application, reducing the cost gap only at the cost of the wide-angle camera. In addition, our approach does not analyze \"writing\" patterns such as those proposed by [PERSON] et al. [15] nor does it send any telematics outside the vehicle such as the SmartSense [12] approach, thus maintaining privacy. If we compare our approach with [PERSON] et al. [15], if changes in the pattern of text messages are used to detect driving, our approach does not require any text message patterns. That is, we can detect when the user is distracted even when the user scrolls down in certain applications, sees a video, plays a game, etc. Furthermore, we do not force the user to enter from a specific door or take the cell phone to a particular place (i.e., the right pocket), according to the research proposed by [PERSON] et al. [18]. Another aspect is that the proposed approach is not based on cloud-based telematics such as [12; 21; 23; 25; 26]; all the processing is done on the site, keeping, as shown, the fixed sensors in the vehicle. They have huge advantages over application-based approaches; however, we do not believe that both approaches fight each other, but we think that the both approaches can benefit from each other; for example, our approach can work backwards (i.e., detect driving instead of sending text messages), and communicate with the mobile phone to inform when the driver was driving, and the mobile phone in combination with other application-based approaches can use that information to block notifications, calls and other distraction events, so that the proposed methodology can be more complete for the user. The industry has driven some approaches based on applications such as TrueMotion, DriveWell, Floow Score, among others [23; 25; 26]. However, these approaches focus on the driver's score to evaluate the risk on the part of the insurance companies. Some of them present some feedback to the driver. In some cases, the user of this feedback reduced the use of the cell phone as in DriveWell [25]. Nevertheless, this change is achieved by increasing the policy insurance or labor penalties for those who use the cell phone. Contrary to this, our approach does not require that the data be collected, transmitted and analyzed to \"score\" a driver, but our approach is based on the detection of users in real time. With this approach, we can send feedback in real time to avoid such use (i.e., steering wheel vibration, dashboard warning, etc.). With this warning, we could reduce the use of the cellphone while driving, and, contrary to industry approaches, we can prevent accidents related to the distraction, and not only calculate a \"risk\" score.