## 4 Discussion  
There is a viable premise in both approaching VLC communications for mobile telephony with applicability in the field of road safety and the use of OCC for detection and analysis, and also data communication with applicability in the same field mentioned above. Most people own smart devices or smart mobile phones, and it is true that many of them do not benefit from superior performance and features in order to categorize this direction as extremely easy to implement. The main purpose was to demonstrate and show that by using the camera, we can have communications and data recovery can have a percentage of about 99% over a distance of 1-5 m. Depending on the increase in \begin{table}
\begin{tabular}{c c c} \hline \hline
**Distance** **(m)** & **Intensity Traffic Light—Red Color** **(lx)** & **Intensity Traffic Light—Green Color** **(lx)** \\ \hline
1 & 4458.0 & 4812.0 \\
5 & 3184.0 & 2445.0 \\
10 & 2134.0 & 1957.0 \\
15 & 1686.0 & 1468.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results regarding the analysis, detection of colors, established thresholds, values measured according to distance and confirmation of message reception. transmission distances, the bit success rate decreases and sometimes exceeds 50-60% over distances of more than 10-15 m, with the mention that the camera sensor has a resolution of approximately 64 megapixels, produced by Samsung. In the first stage, the results are satisfactory, but they are closely related to the performance of the cameras. The success rate of bits over short distances is remarkable, reaching 100% on a measured length of 80-100 cm. We cannot say that the binary process is a complete one and that it completely excludes the backlight or the illumination of the screen; solutions in these cases are the automatic adjustment and the calculation of the threshold for the mediation between the optimal illumination and the incidence area. The first stage was materialized by the Otsu method, and it was demonstrated that the performance of such a solution could be optimized even if there are disturbing areas or factors that negatively influence it. Direct exposure to ambient light and fluctuating threshold level +600 lux can disrupt the decision. We can also point out that there were trends in which the color black offered a strong shine compared to the white one, making a much more difficult transition in the process of schematic comparison. The geometric loading and setting of FOV in the process variation of the Tx rotation--where the devices were positioned in the same direction--were also performed. Regarding the secondary stage in which Android Studio and OpenCV conditioned with the use of ambient light sensors were used, the results are to some extent viable with the specification that in strong reflections there is a degree of saturation of the sensor. The purpose of this direction was clearly mentioned in the text of the manuscript. Due to the multitude of road events in which people lose their lives due to inattention and excessive use of mobile phones, outlining an application that can communicate information through visible light and interpret light frequencies as dangerous is imperative. Therefore, the results obtained can be described as useful in the conditions in which a mobile device has an ambient light sensor without too much capacity at a low refresh rate and cannot even adapt directly to light. It cannot be compared to an experiment in which there is a VLC reception system based on PIN photodiodes with another FOV and completely different characteristics. Under the given conditions, decoding of the signals and identification of the light frequencies at distances of approximately 15 m from a traffic light system in a time of approximately 8-11 ms were obtained. Human reactions are usually extremely delayed, and the analysis process compared to a pedestrian's decision change, in this case, reflects reality.