# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 4 Proposed Study
### Proposed Feature Extraction Models
#### 4.2.3 Proposed Hybrid Fusion Model
Root mean square error (RMSE) values of the images obtained after edge detection are estimated. The lower the RMSE values are, the higher is the accuracy of the constructed image. The accuracy of the proposed edge detection model is better than the other popular edge detection models. RMSE is calculated using Equation (23), and Table 6 illustrates the RMSE values obtained from different edge detection models for different images. The results show that the proposed edge detection model provides a lower value of RMSE, which indicates the improved accuracy in image construction over the other popular edge detection models. Figure 14 illustrates the results obtained after implementing the proposed and other popular edge detection models. From the results, it is evident that the images constructed after implementing the proposed approach are better than other popular edge detection models. \[RMSE=\sqrt{\frac{1}{MN}\sum_{r=0}^{N-1}\left[E(r,c)-o(r,c)\right]^{2}} \tag{23}\]

In another comparison, advanced machine learning models are used to compare the classification accuracy of the fused images obtained from different fusing models, namely principle component analysis (PCA), weighted median filter-based Gram-Schmidt transform (WM-FGS) and Gram-Schmidt (GS). The visual differences between different classification results are portrayed in Figure 15. The CNN model using the random forest (RF) classification model is used to classify the fused images obtained from the four fusion models. The CNN model designed has three layers: one for preprocessing, the next layer for fusion, and the final layer for classification. The proposed feature extraction model and fusion models are converted into appropriate kernel functions and implemented in the first two layers. The random forest classification model is a combination of self-relying decision trees, and decision trees handle alphanumeric data easily when compared with other classification models. From the sample of a thousand fused images obtained from the four fusion models, seven hundred samples are used to train and three hundred samples for testing the RF model. The following steps are carried out to build the infrastructure of the CNN model. The first stage is data acquisition, followed by data preprocessing. Subsequently, the RF classification model using sklearn and NumPy python packages is implemented to train and test the sample fused images obtained by the four fusion models used in this study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Images** & **Model** & **SD** & **AG** & **SF** & **PSNR** & **CC** \\ \hline \multirow{8}{*}{1} & GS & 305.3385 & 9.4444 & 60.0375 & 89.6709 & 0.6908 \\  & HIS & 363.5207 & 9.5403 & 133.8691 & 95.5200 & 0.7078 \\  & Subtractive & 375.7824 & 9.4150 & 133.3344 & 94.5325 & 0.7107 \\  & Proposed & 380.298 & 10.2148 & 135.6382 & 96.2867 & 0.9472 \\  & GS & 289.3498 & 9.6105 & 78.5329 & 90.0781 & 0.6040 \\  & HIS & 325.7555 & 9.7290 & 177.7829 & 93.0075 & 0.6077 \\
2 & Subtractive & 339.9461 & 10.4368 & 178.6462 & 93.0360 & 0.6360 \\  & Proposed & 365.4752 & 11.4976 & 180.5643 & 94.2335 & 0.8235 \\  & GS & 625.3025 & 10.5363 & 131.9687 & 114.0561 & 0.7434 \\  & HIS & 646.8171 & 10.8526 & 132.6416 & 95.551 & 0.7313 \\
3 & Subtractive & 657.6024 & 9.9434 & 120.6161 & 95.6224 & 0.7344 \\  & Proposed & 370.2539 & 11.3145 & 123.6213 & 93.25 & 0.6543 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall performance of proposed and other popular image fusion models for various key metrics. ### Overall Analysis of Proposed GAN Fusion Model

In the extended work (this study), the proposed GAN model has been included to convert the proposed hybrid image fusion model into a machine learning model to handle huge volumes of image data, specifically point cloud data. The proposed GAN model is an advanced CNN model, which is designed as a single convolutional model layer that includes five main layers.