## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Color**} & **Distance** & **Degree** & **Estimated** & **Real** & **Accuracy** \\  & **(m)** & **(\({}^{\circ}\))** & **Intensity** & **Intensity** & **(\%)** \\ \hline \multirow{4}{*}{SW-Gloss (White)} & \multirow{2}{*}{2.5} & 0 & 97.660 & 96.714 & 99.629 \\  & & 5 & 97.495 & 96.174 & 99.482 \\  & & 0 & 95.561 & 93.715 & 99.276 \\  & & 5 & 95.034 & 93.486 & 99.393 \\ \hline \multirow{4}{*}{SB-Matte (Black)} & \multirow{2}{*}{2.5} & 0 & 30.109 & 32.238 & 99.165 \\  & & 5 & 19.863 & 18.392 & 99.423 \\  & & 0 & 45.632 & 48.838 & 98.743 \\  & & 5 & 33.851 & 31.648 & 99.136 \\ \hline \multirow{4}{*}{SB-Gloss (Black)} & \multirow{2}{*}{2.5} & 0 & 1.751 & 1.601 & 99.941 \\  & & 5 & 1.274 & 1.173 & 99.960 \\  & & 0 & 3.125 & 2.956 & 99.934 \\  & & 5 & 1.962 & 1.840 & 99.952 \\ \hline \multirow{4}{*}{CDSBL-Gloss (Blue)} & \multirow{2}{*}{2.5} & 0 & 48.727 & 48.224 & 99.803 \\  & & 5 & 48.150 & 47.971 & 99.930 \\  & & 0 & 50.015 & 49.295 & 99.713 \\  & & 5 & 47.894 & 48.186 & 99.886 \\ \hline \multirow{4}{*}{TCSRM-Gloss (Red)} & \multirow{2}{*}{2.5} & 0 & 74.861 & 78.191 & 98.694 \\  & & 5 & 68.862 & 66.267 & 98.982 \\  & & 0 & 80.277 & 82.602 & 99.088 \\  & & 5 & 74.454 & 72.763 & 99.337 \\ \hline \multirow{4}{*}{SMRTG-Gloss (Green)} & \multirow{2}{*}{2.5} & 0 & 75.349 & 73.268 & 99.184 \\  & & 5 & 74.193 & 72.659 & 99.398 \\ \cline{1-1}  & & 0 & 81.113 & 78.602 & 99.015 \\ \cline{1-1}  & & 5 & 79.140 & 77.831 & 99.487 \\ \hline \multirow{4}{*}{TSSM-Gloss (Silver)} & \multirow{2}{*}{2.5} & 0 & 99.930 & 101.064 & 99.555 \\ \cline{1-1}  & & 5 & 97.211 & 97.235 & 99.991 \\ \cline{1-1}  & & 0 & 100.827 & 102.497 & 99.345 \\ \cline{1-1} \cline{2-6}  & & 5 & 96.849 & 99.010 & 99.153 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the estimated intensity values obtained from the developed GPR and curve-fitting model with the actual intensity values reported by [PERSON] et al. [6]. above 98% across all surfaces demonstrates its strong predictive performance, even on surfaces with varying reflectivity properties. For 2.5 m and 5\({}^{\circ}\), the model maintained similarly high accuracy levels. The highest accuracy was recorded for the SB-Gloss (Black) surface at 99.960%, while the lowest accuracy was 98.982% for the TCSRM-Gloss (Red) surface. In this angular range, the model exhibited an accuracy above 99% for the other colors, highlighting its robustness against angular variations. For a distance of 5 m and an angle of 0\({}^{\circ}\), the model once again demonstrated strong performance, achieving the highest accuracy of 99.991% on the TSSM-Gloss (Silver) surface. The lowest accuracy was recorded at 99.015% for the SMRTG-Gloss (Green) surface. Despite the increased distance, the model maintained an accuracy above 99%, particularly for glossy and metallic surfaces, indicating its consistency in handling light reflectivity properties. For 5 m and 5\({}^{\circ}\), the highest accuracy was observed on the SMRTG-Gloss (Green) surface at 99.487%, while the lowest accuracy was 99.153% on the TSSM-Gloss (Silver) surface. These accuracy levels demonstrate that, even at a distance of 5 m, the model maintains a highly stable prediction performance. Seven different measurements from the CUPAC dataset were analyzed in detail to identify scenarios that match the vehicle colors examined in this study. First, the camera images corresponding to each measurement were reviewed, and the PCD data of the selected scenarios were processed in Matlab. During this process, the laser intensity distribution was visualized along with the vehicle's position and surrounding objects. Subsequently, spatial filtering was applied to isolate laser reflections only from the vehicle's surface, effectively eliminating the influence of surrounding objects on the measurements. As a result, only the laser intensity values reflected from the vehicle surface were obtained.