# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 3 Related Work
### Feature Extraction Models (Image Data)  
[PERSON] et al. [32] have proposed a novel universal image feature extraction approach called variational image domain analysis, which is used for a wide range of very long baseline interferometry (VLBI) image reconstructions. Variational image domain analysis, unlike earlier methods, may be used for any image reconstruction, independent of its structure. The authors' approach gives clear ideas on how to extract salient image features such as color and edge. [PERSON], [PERSON] et al. [33] have customized a CNN model to extract deep features of images related to food. The CNN model, when paired with nondestructive detection techniques and a computer vision system, has great potential for identifying and analyzing complex food matrices. CNN-based features outperform handmade or machine-learning-based features. [PERSON] et al. [34] have proposed a multi-view structural feature extraction approach to provide a thorough characterization of spectral-spatial structures of various objects, which consists mostly of the stages below. First, the original image's spectral number is reduced using the minimum noise fraction (MNF) approach, and then the local structural feature is extracted from the dimension-reduced data using a relative total variation. The nonlocal structural characteristics from intra-view and inter-view are then produced using a superpixel segmentation approach that takes into account the intra- and inter-similarities of superpixels. The final picture characteristics for classification are formed by combining the local and nonlocal structural features. [PERSON] et al. [35] have presented a texture feature extraction approach with increased discriminating power for volumetric pictures. The technique is used to classify textured volumetric data. The authors employ feature vectors obtained from local binary patterns (LBP) and the gray-level co-occurrence matrix-based approach to combine two complementing types of information. [PERSON] et al. [36] have provided a feature extraction methodology for human activity recognition that is both efficient and low in dimension. The enveloped power spectrum (EPS) is employed in this feature extraction approach to recover impulse components of the signal utilizing frequency domain analysis, which is more robust and noise intolerant. For human activity recognition, linear discriminant analysis (LDA) is utilized as a dimensionality reduction approach to extract the smallest amount of discriminant characteristics from the envelope spectrum, human activity recognition (HAR). A multi-class support vector machine (MCSVM) is used to recognize human activities using the derived characteristics. To extract robust features, [PERSON] et al. [37] have used a stacked convolutional denoising autoencoder (SCDAE), which reduces susceptibility to partially damaged data, or inputs data that are partially missing. Trial-and-error experiments were used to optimize SCDAE parameters such as network depth, number of convolution layers, number of convolution kernels, and convolution kernel size. The analysis identified some gaps in both the explored image fusion models and feature extraction models. Regarding image fusion, most of the referred image models do not produce better accuracy. A generic fusion model to fuse all formats of data is missing. Most referred studies use the minimum dataset to evaluate their models. Complicated operations such as image transformation (2D to 3D) and other image functionalities are not transparent in many studies. Moreover, there are minimum contributions related to multispectral image fusion. Most of the referred models are complex and require advanced algorithms and techniques. There is a need for developing computationally efficient fusion algorithms that can operate in real-time or near real-time scenarios without sacrificing the quality of the fused images. Exploring techniques such as model compression, hardware acceleration, and parallel processing can help bridge this gap.