## 2 Materials and Methods
### Evaluation of Model Performance Using Real-World Data  
In this section, measurements and scenarios from the CUPAC dataset were analyzed to evaluate the performance of the developed model during real driving conditions. Additionally, this analysis aimed to assess the model's performance on a different LIDAR device. In the dataset, only vehicle scenarios corresponding to the color charts used in this study were examined. To prevent environmental effects from being incorporated into the model and to ensure that only vehicle-induced effects were included in the analysis, a spatial filtering process was applied. This filtering process selected only the points within specific x, y, and z boundaries where the vehicle was located. After filtering, the reconstructed PCD data were isolated from surrounding objects and ground effects. Additionally, in the study conducted by [PERSON] et al., it was stated that the outdoor experiments were conducted in open-air conditions during the daytime. Therefore, when selecting appropriate scenarios from the CUPAC dataset, care was taken to ensure similar environmental conditions, and measurements conducted in open-air and daytime settings were preferred. Moreover, the CUPAC dataset does not include any labeling or information regarding the age of the vehicles. Similarly, in the study conducted by [PERSON] et al., which was used as a reference for model training, there was no evaluation of the age of the test panels. For this reason, the effect of the age factor was omitted in this study [6; 16]. To accurately analyze the data obtained from LIDAR measurements, the distance and angle between the vehicle and the sensor were calculated using trigonometric transformations based on three-dimensional coordinate data obtained after spatial filtering. In these calculations, both the vertical (\(\phi\)) and horizontal (\(\theta\)) angles were considered, and this information was provided as input parameters to the model. Furthermore, the role of the horizontal angle between the vehicle and the sensor in determining the effective laser channels was evaluated. The distance parameter was calculated using Equation (5), where x, y, and z represent the three-dimensional spatial coordinates of the vehicle relative to the sensor. \[\text{r}=\sqrt{\text{x}^{2}+\text{y}^{2}+\text{z}^{2}} \tag{5}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Time (min)** & **Traff. D.** & **Traff. P.** & **Weather** & **Time** & **Features** \\ \hline a1 & 6.4 & High & High & Cloudy & Afternoon & Inner-city, Parking lot \\ \hline a2 & 8.3 & Low & High & Cloudy & Afternoon & Parking lot, Country road \\ \hline a3 & 8.3 & High & Low & Cloudy & Afternoon & Inner-city, Country road \\ \hline a4 & 8.3 & High & Medium & Cloudy & Afternoon & Inner-city \\ \hline b1 & 6.28 & Medium & Medium & Sunny & Noon & Inner-city \\ \hline b2 & 16.92 & Low & High & Sunny & Noon & Residential area, Road bumps \\ \hline b3 & 25.38 & Medium & High & Sunny & Noon & Residential area, Road bumps, Inner-city \\ \hline \end{tabular}
\end{table} 
Table 5: Dataset scenario summary. The vertical angle (\(\phi\)) defines the up/down axis orientation of the vehicle relative to the sensor and directly corresponds to the \"angle\" parameter in the model. The vertical angle was calculated using Equation (6). \[\phi=\tan^{-1}\left(\frac{\mathrm{z}}{\sqrt{\mathrm{x}^{2}+\mathrm{y}^{2}}}\right) \tag{6}\]  
The horizontal angle between the vehicle and the sensor determines the vehicle's position within the sensor's scanning field on the horizontal plane. This angle was calculated using Equation (7). \[\theta=\tan^{-1}\left(\frac{\mathrm{y}}{\mathrm{x}}\right) \tag{7}\]  
The calculations in Equations (5)-(7) were individually applied to all selected points, and the averaged values were used to determine the vehicle's overall distance and angle relative to the sensor. The reference position of the LIDAR was obtained from the coordinates provided in the CUPAC dataset [16].