# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 5 Results and Discussion
### Comparative Analysis  
The comparative analysis of the TLFC-RD method with existing research is presented in this section. The TLFC-RD method was compared with four different existing methods: U-Net-DAM [17], TFCN [18], TAAUWN-MV [20] and TAAUWN-MVP [20]. The aforementioned existing methods were chosen for comparison because all of these four methods use deep learning architectures for road detection. Similarly, the proposed TLFC-RD method also uses deep learning classifiers during road detection. Additionally, these existing methods process the road scenes with different light intensities in the same manner as the TLFC-RD method. The comparison was made by using two different datasets: KITTI and CamVid datasets. Here, the efficiency of the TLFC-RD method was evaluated by using k-fold cross-validation, where k = 10. Additionally, a unified activation function, ReLU, was considered to perform a fair comparison between the TLFC-RD method and existing methods. In that case, the existing methods--U-Net-DAM [17], TFCN [18], TAAUWN-MV [20] and TAAUWN-MVP [20]--were implemented and simulated using the same activation function. Tables 4 and 5 show the performance comparison of the TLFC-RD method for the KITTI dataset and CamVid dataset, respectively, where NA represents the parameters that were not available for the existing methods. Moreover, the comparison for the KITTI dataset and CamVid dataset with a unified activation function is shown in Tables 6 and 7, respectively. The accuracy comparison of the TLFC-RD method for the KITTI dataset with same activation function is shown in Figure 7. From the analysis, we can see that \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **ACC (\%)** & \begin{tabular}{c} **Sensitivity** \\ **/Recall (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} & **PRE (\%)** &
\begin{tabular}{c} **F1-Measure** \\ **(\%)** \\ \end{tabular} & **MaxF (\%)** \\ \hline ANN & 88.35 & 90.13 & 92.12 & 90.43 & 91.35 & 93.21 \\ SVM & 91.35 & 92.38 & 93.04 & 91.76 & 92.98 & 93.04 \\ RFC & 92.41 & 91.89 & 92.04 & 92.43 & 91.08 & 94.21 \\ Lenet-5 & 96.75 & 95.48 & 92.39 & 95.44 & 94.11 & 97.05 \\ ResNet & 97.45 & 96.17 & 95.76 & 95.83 & 96.79 & 97.58 \\ TLFC-RD & 99.25 & 98.63 & 96.37 & 97.45 & 98.33 & 97.27 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance analysis of the TLFC-RD method for extreme sunlight images from the KITTI dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **ACC (\%)** & \begin{tabular}{c} **Sensitivity** \\ **/Recall (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Specificity** \\ **(\%)** \\ \end{tabular} & **PRE (\%)** &
\begin{tabular}{c} **F1-Measure** \\ **(\%)** \\ \end{tabular} & **MaxF (\%)** \\ \hline ANN & 90.12 & 91.43 & 90.32 & 93.21 & 90.82 & 91.25 \\ SVM & 91.24 & 92.15 & 94.06 & 93.22 & 95.01 & 92.08 \\ RFC & 92.96 & 94.02 & 93.64 & 92.73 & 93.42 & 92.75 \\ Lenet-5 & 96.13 & 95.11 & 95.88 & 96.04 & 95.61 & 96.25 \\ ResNet & 97.81 & 96.25 & 96.33 & 96.92 & 97.24 & 96.03 \\ TLFC-RD & 99.24 & 98.72 & 97.01 & 98.93 & 98.75 & 96.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance analysis of the TLFC-RD method for CamVid dataset. the TLFC-RD method achieves improved performance compared to U-Net-DAM [17], TFCN [18], TAAUWN-MV [20] and TAAUWN-MVP [20]. The reasons for the existing methods exhibiting poorer performance are as follows: the variations in the environment affected the performances of U-Net-DAM [17], while TFCN [18] and TAAUWN [20] achieved poorer performances because they used fewer features during the road detection. However, the performance of the TLFC-RD method was improved by using a two-level fusion of the classifiers, where deep learning methods (LeNet-5, LSTM and ResNet) were used to classify the images. Additionally, the TLFC-RD method was not affected by changes in the environment because it used appropriate features from the multi-classifier feature fusion model to improve the classification.