# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 3 Related Work
### Feature Extraction Models (Image Data)
Implementing and fine-tuning these models can be challenging, requiring significant computational resources and expertise. Image fusion is a subjective task, and the quality of the fused image can vary depending on individual preferences and application requirements. Most referred models involve multiple parameters and design choices, making it difficult to determine an optimal fusion result that satisfies everyone. There is still room for exploring more efficient and effective deep-learning architectures specifically designed for hybrid image fusion. Research should focus on developing novel network architectures, attention mechanisms, and loss functions that can capture complementary information from multiple input images and improve the fusion quality. Most models often lack interpretability and explainability. It is challenging to understand the decision-making process and the contribution of different input images in the fusion result. Further research is needed to develop techniques that can provide insights into the fusion process, visualize the information fusion at different stages, and offer explanations for the final fusion outcome. Regarding the referred feature extraction models, most of the referred models are complicated and require high computational costs. Most models depend on CPU utilization time and memory. Further, the feature detection models depend on the experiences of the designer. Having the specified gaps as the point of motivation, this study proposes a generic data fusion engine to fuse all formats of data and also proposes innovative strategies to extract the salient features of image and audio data. Many feature extraction models are trained and optimized for specific datasets or domains. However, there is a need for models that can generalize well across different domains, such as medical imaging, natural images, satellite imagery, and more. Developing domain-agnostic feature extraction models that can capture and represent diverse types of data effectively remains a challenge. With the increasing demand for real-time and large-scale applications, there is a need for feature extraction models that are efficient and scalable. Developing lightweight architectures and techniques for efficient feature extraction, model compression, and hardware acceleration is an ongoing research direction to enable faster and more resource-efficient feature extraction. To address the identified gaps, this study introduces effective models for feature extraction that can extract image features from all types of image data. Additionally, a hybrid image fusion model is proposed to fuse 2D and 3D multispectral image data. The study presents advanced projection and image transformation formulas to enhance the efficiency of the image fusion process. However, the performance of the proposed hybrid image fusion model is found to be unsatisfactory when applied to 3D point cloud data and when dealing with large image datasets. To overcome these limitations, the study expands its scope by proposing an innovative image fusion model that incorporates advanced concepts from the GAN (Generative Adversarial Network) model. This new model performs various tasks, including advanced feature extraction to capture both spatial and spectral information, as well as generator and discriminator modules to facilitate fusion tasks while preserving image quality. Customized kernel functions are introduced for the CNN (Convolutional Neural Network) layers to execute the specified tasks.