# 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
However, the proposed GAN model has its own limitations, such as training instability, which necessitates separate training for generators and discriminators. While the assigned parameters for initializing GAN layers performed well with the GeoTiles dataset, further evaluations are needed to assess the model's ability to fuse images from other datasets. Considerable time was dedicated to optimizing the hyperparameters employed for initializing the model. The study employed a minimal number of images for training and testing the model, and its performance with extensive datasets should be validated in future research. ## 6 Conclusions

Providing security to customers is a vital task for both AVs and manually driven vehicles. Most of the researchers who propose innovative solutions for AVs have their major focus on providing proper safety and security to the users. This proposal analyzed various reasons for the failure of full-fledged AVs in the current market, which are launched after many trials and research activities. The proposed study found that most of the recently launched AVs failed due to inaccurate and improper decision-making policies, due to their poor perception of their environmental information. Hence, much emphasis has to be given to data preprocessing, namely data cleaning and data fusion of AVs' sensory data. This research suggests a generic data fusion engine to fuse different formats of data, such as text, images, video, and audio, which exhibit multimodal characteristics. The first stage of the research focused heavily on image fusion since most modern sensors, such as LiDAR and Velodyne, collect data in the form of image frames represented as point cloud data. To facilitate this scenario, this study proposed an innovative hybrid image fusion model, which integrates the concepts of the Brovey fusion model and Gram-Schmidt transformation. The suggested image fusion model is robust enough to handle multispectral image data, retain the RGB color intensity of the fused images, and perform image transformation more efficiently. The proposed hybrid image data fusion model was evaluated with the nuScenes dataset and its performance was compared using standard image metrics with other referred and popular data fusion models. The proposed image fusion model provides better results than the other standard image fusion models in terms of accuracy and efficiency. Despite the proposed hybrid fusion model demonstrating proficiency in handling 2D and 3D data, it encountered difficulties when dealing with 3D point cloud data and managing large volumes of image data. To address these challenges, the study extended its research by introducing an innovative GAN model capable of handling diverse image data types. In addition to conventional metrics, new metrics such as mutual information and entropy were employed to evaluate the performance of the proposed GAN model. Evaluation of the GAN model utilized point cloud data extracted from the GeoTiles dataset. The performance of the proposed GAN model was compared with other popular fusion models that extensively utilize point cloud data. The results revealed that the proposed GAN model outperforms other fusion models. To enhance the proposed fusion model further, future work entails fine-tuning and evaluating it using different datasets to assess its improved performance. Additionally, plans are underway to enhance the GAN architecture by incorporating principles of multiscale attention and advanced discriminators to improve feature selection and data fusion accuracy. Furthermore, efforts will be made to design and develop advanced kernel functions for the various tasks performed by different layers of the GAN model. Further analysis is required to check whether the accuracy of the fused data enhances perception creation for accurate decision-making in AVs. Future work couldlook into incorporating the video and audio fusion models, which are currently underway, into the proposed framework. H.A.I.