# 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
Different categories of comparisons are carried out to evaluate the proposed models, and Table 4 displays various key metrics used to evaluate the proposed and other popular image fusion models. ### Performance of Hybrid Image Fusion Model with Other Studies

The proposed model is compared with other studies [50; 51] that use hybrid approaches to fuse image data. From the observations, the proposed hybrid image fusion model produces a more accurate fused image than the other studies. From the results portrayed in Figure 11 it is evident that the image clarity and resolution of the proposed image fusion are better than the referred hybrid fusion models. \begin{table}
\begin{tabular}{c c} \hline
**Metric Name** & **Purpose** \\ \hline Accuracy (Acc) [47] & To compare accuracy \\ Efficiency (Eff) [47] & To evaluate the performance of the models \\ Standard Deviation (SD) [48] & To estimate the contrast \\ Average Gradient (AG) [47] & To express small detail contrast and texture changes, as well as the sharpness of the image \\ Spatial Frequency (SF) [47; 48] & To measure the overall activity level of the image \\ Peak Signal-to-Noise Ratio (PSNR) [49] & To compute the visual error between the fused image \\ Correlation Coefficient (CC) [48] & To find the similarity between the reference image \\  & and the fused image \\  & Is an indicator of how closely the two unrelated factors are related. It measures how knowledge difference between two random factors \\  & Entropy (EN) [49] & Estimates uncertain index \\ \hline \end{tabular}
\end{table} 
Table 4: Key metrics used in the research to evaluate image fusion models. \begin{table}
\begin{tabular}{c c c} \hline
**S. No** & **Tools** & **Version** & **Description** \\ \hline
1 & PyCharm & 2.7 & IDE \\
2 & Python \& Libraries (Pandas, NumPy, & 3.10.4 & Development of the models \\
3 & Open3D, Shapely) & & \\
3 & MATLAB & R2021a & Developing applications \\
4 & Vehicle perception software system & 1.0 & To create 3D point cloud vehicle perception \\
5 & Mogo DB & 5.0 & Database \\
6 & Linux & 5.4. 0–26 & Operating system \\
7 & Weka & 3.0 (GPLv3) & Preprocessing \\
8 & Dataset & nuScenes, Geolles (For 3D point & To evaluate the proposed image fusion model \\  & & cloud data) & \\ \hline \end{tabular}
\end{table} 
Table 3: Various tools used for experimental analysis. Further, the accuracy and efficiency of the proposed hybrid fusion model are compared with other standard fusion models [50; 51]. The accuracy of the fused images derived from different models is calculated using Equation (22), and efficiency is estimated by the time taken by the fusion model to fuse the images. The results obtained are portrayed in the below Figures 12 and 13. The results prove that the accuracy and efficiency of the proposed image/video fusion models are better than the other popular image/video fusion models. The proposed hybrid image/video fusion models achieve more than 94% accuracy after fusing the images with minimum resolution and clarity. Further, the proposed hybrid fusion models consume less CPU time to fuse the images over the other referred fusion models. \[Accuracy=\frac{Pixel_{intensity}(Original)}{Pixel_{intensity}(Fused)} \tag{22}\]

Figure 11: Comparison with other referred models. Figure 12: Accuracy comparison with other models. Figure 13: Efficiency comparison. The final fused images obtained using the proposed and other popular fusion models are evaluated using various metrics including standard deviation, average gradient, spatial frequency, peak signal-to-noise ratio, and correlation cofactor. A sample of three fused images obtained from different models is considered for evaluation. The proposed fusion models perform better than the other popular fusion models. Table 5 portrays the results obtained after evaluation. The impact of the metrics can be visually seen in Figures 14 and 15. The study also evaluated the proposed edge-detection model with other popular edge-detecting models such as KFA (Kalman filter algorithm) and Sobel & Prewitt.