## 4 Performance Evaluation  
In this section, the road classification performance of the MODAE-RCM method is investigated, utilizing a dataset which comprises 12,500 samples with five types of roads, as depicted in Table 1. Figure 3 demonstrates some sample images. \begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **No. of Samples** \\ \hline Dry & 2500 \\ \hline Ice & 2500 \\ \hline Rough & 2500 \\ \hline Wet & 2500 \\ \hline Curvy & 2500 \\ \hline Total No. of Samples & 12,500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset details. The confusion matrices provided by the MODAE-RCM method on the road classification process are portrayed in Figure 4. The figure highlighted in the MODAE-RCM method has categorized all the types of roads accurately. Table 2 and Figure 5 represent the overall road classification outcomes of the MODAE-RCM approach on the entire dataset. The outcomes reported the MODAE-RCM model has proficiently recognized all of the five distinct kinds of roads on the applied input images. It can be noticed that the MODAE-RCM model has offered an average \(accu_{y}\) of 99.29%, \(sens_{y}\) of 98.23%, \(spec_{y}\) of 99.56%, \(F_{score}\) of 98.23%, and \(AUC_{score}\) of 98.89%. Figure 3: Sample images. Figure 4: Confusion matrices of MODAE-RCM system: (**a**) entire database, (**b**) 70% of time series database (TR) database, and (**c**) 30% of time series (TS) database. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Entire Dataset**} \\ \hline
**Class** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** & **AUC Score** \\ \hline Dry & 99.22 & 98.20 & 99.48 & 98.06 & 98.84 \\ \hline Ice & 99.61 & 98.80 & 99.81 & 99.02 & 99.30 \\ \hline Rough & 99.50 & 98.76 & 99.68 & 98.74 & 99.22 \\ \hline Wet & 98.97 & 96.64 & 99.55 & 97.40 & 98.09 \\ \hline Curvy & 99.17 & 98.76 & 99.27 & 97.94 & 99.02 \\ \hline Average & 99.29 & 98.23 & 99.56 & 98.23 & 98.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Road classification outcomes of the MODAE-RCM system with varying classes under the entire database. Table 3 and Figure 6 signify the overall road classification outcomes of the MODAE-RCM method on 70% of the TR database. These results indicate that the MODAE-RCM approach has proficiently recognized all of the five distinct kinds of roads in the applied input images. It is noted that the MODAE-RCM approach has presented an average \(accu_{y}\) of 99.29%, \(sens_{y}\) of 98.23%, \(spec_{y}\) of 99.56%, \(F_{score}\) of 98.2  
Table 4 and Figure 7 portray the overall road classification outcomes of the MODAE-RCM approach on 30% of the TS database. These outcomes indicate that the MODAE-RCM method has proficiently recognized all of the five distinct kinds of roads on the applied input images. It is noted that the MODAE-RCM approach has rendered an average \(accu_{y}\) of 99.30%, \(sens_{y}\) of 98.25%, \(spec_{y}\) of 99.56%, \(F_{score}\) of 98.24%, and \(AUC_{score}\) of 98.90%. The training accuracy (TRA) and validation accuracy (VLA) gained by the MODAE-RCM technique under the test database are exemplified in Figure 8. The experimental result denotes that the MODAE-RCM method has reached maximal values of TRA and VLA. Principally, the VLA is greater than the TRA. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Testing Phase (30\%)**} \\ \hline
**Class** & **Accuracy** & **Sensitivity** & **Specificity** & **F-Score** & **AUC Score** \\ \hline Dry & 99.25 & 98.64 & 99.40 & 98.10 & 99.02 \\ \hline Ice & 99.65 & 98.93 & 99.83 & 99.13 & 99.38 \\ \hline Rough & 99.57 & 99.21 & 99.67 & 98.94 & 99.44 \\ \hline Wet & 98.88 & 95.78 & 99.67 & 97.19 & 97.72 \\ \hline Curvy & 99.12 & 98.68 & 99.23 & 97.84 & 98.96 \\ \hline Average & 99.30 & 98.25 & 99.56 & 98.24 & 98.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Road classification outcomes of the MODAE-RCM system with varying classes under 30% of the TS database. Figure 6: Road classification outcomes of the MODAE-RCM system under 70% of the TR database.