# Mono Camera-Based Optical Vehicular Communication for an Advanced Driver Assistance System
## 4 Results  
We constructed a vehicle taillight prototype to assess the performance of our proposed system economically. The implementation parameters are listed in Table 1. The taillight pair was randomly moved at three different orientations, i.e., left, right, and straight; a set of implementation results is shown in Figure 9. The implementation results were verified by measuring the deviation with the actual values. The deviations were under the tolerance limit, which was set at 5\({}^{\circ}\). Figure 10 shows that the MSE in angle calculation decreased as the number of iterations increased. The iteration rate was varied up to 1200 iterations to select the optimum learning rate. The learning rate changed very slowly after iteration 700 and became nearly constant around iteration 1100. The learning rate was set at 0.001. Figure 8: Hypothetical triangle for calculation of bending angle when the FWV turns (**a**) left and (**b**) right. The distance estimation accuracy was also determined which is shown in Figure 11. The distance of the FWV from the FLV was estimated via necessary experiments and \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Parameter** & **Value** \\ \hline \multirow{4}{*}{Camera} & Image resolution & 1080 \(\times\) 1920 \\ \cline{2-3}  & Camera frame rate & 30 fps \\ \cline{2-3}  & Exposure time & 2.5 ms \\ \hline \multirow{3}{*}{Backlight prototype} & Distance between two LEDs & 10 cm \\ \cline{2-3}  & LED size (diameter) & 3 mm \\ \cline{2-3}  & Maximum distance & 50 m \\ \hline \multirow{4}{*}{CNN} & Subdivisions & 16 \\ \cline{2-3}  & Exposure & 1.5 \\ \cline{2-3}  & Learning rate & 0.001 \\ \cline{2-3}  & Maximum batches & 8000 \\ \cline{2-3}  & Classes & 18 \\ \cline{2-3}  & Filters & 69 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Implementation parameters. Figure 10: Reduction of error in angle calculation with the number of iterations. Figure 9: The projected rear LED image of FWV on the image sensor of FLV when it enters into a road that (**a**) curves in the left direction, (**b**) goes straight, and (**c**) curves in the right direction. training with the previously recorded data. For identical rear LEDs of an FWV, the detected contour size would increase if an FLV approached it, and vice versa. Therefore, an experiment was performed to record the LED contour area and the corresponding distances. For instance, a circular LED with a diameter of 3 mm was considered, and 500 samples were recorded within a range of 50 m. The system was tested in a scenario, and the distance was measured by comparing it with the dataset. The accuracy decreased sharply when the distance increased to over 20 m because the change in the detected LED contour size decreased after a certain distance was reached. The inaccuracy of detecting the pixel coordinates increased as the distance increased. As a result, the estimation accuracy of the low-resolution camera was unsatisfactory at a large distance. At a long distance, a better approximation can be achieved by diving the pixels into \(n\) segments or using a high-resolution camera. The OCC data were decoded from the light intensity received in the IS. In the case of a single LED transmitter, bright and dark stripes were generated according to the ON and OFF states of the LED owing to the camera's rolling shutter effect. In our work, two types of bright stripes were generated, one for the high-power-level signal and the other for the low-power-level signal. The stripe patterns received in the IS are shown in Figure 12. The thickness of the stripes was dependent on the frequency of the modulated signal. The high power signals created a brighter stripe than did the low power signals in the IS. Every two consecutive high-frequency data bits' amplitude was modulated according to the low-rate stream. Two different threshold levels (i.e., \(Th_{L}\) and \(Th_{H}\)) were used to retrieve the transmitted data from the stripe pattern.