# A Generic Framework for Enhancing Autonomous Driving Accuracy through Multimodal Data Fusion
## 3 Related Work
### Hybrid Image Fusion Models
Since the study has extended its work by proposing a versatile GAN model to fuse all types of advanced image data, the research has explored the contributions of researchers in image fusion using GAN models. Reference [23] have proposed a novel hybrid image fusion model using GAN techniques called as PAN-GAN. The author's model is used to fuse panchromatic images. The PAN-GAN model uses a separate adversarial mechanism that establishes a bond with the discriminators to preserve the spectral and spatial information of the fused images. Similarly reference [24] have proposed an innovative fusion model, namely GAN-FM, which uses the GAN principle to fuse infrared and visible images. The authors have designed a full-scale skip-connected generator along with discriminators based on Markovian principles for extracting features at different scales and to establish a link with the generators to retain the contrast of the fused images. In yet another interesting study, reference [25] have proposed an innovative hybrid image fusion model, namely THFuse, which uses GAN approaches to fuse infrared and visible images. The authors have used advanced fusion strategies such as transformer and hybrid feature extraction concepts to process both global and local image information. Reference [26] have proposed a versatile image fusion model called mask deep fusion network for visible and infrared image fusion (MDFN). The authors have proposed a novel mechanism to compute the weight score for every pixel to estimate the contributions of the two input source images. This operation transfers valuable information from source to fused images, helping them to retain their contrast. Reference [27] have suggested a hybrid image fusion model, namely the pair feature difference guided network (FDGNet), to fuse multimodal medical images. The authors have proposed a weight-guided mechanism to extract the features from complex medical images efficiently. Further, the authors have introduced a factor, namely hybrid loss, composed of weight fidelity loss and feature difference loss to train the network effectively. Since this study focuses on the feature extraction process, detailed literature related to some of the proposed image feature extraction methods is analyzed. This proposed study plans to extract four important image features, namely color, edge, height, and width. In their recent publication, [PERSON] et al. [28] introduced a cutting-edge generative adversarial network named MSAt-GAN. This novel model incorporates multiscale feature extraction and deep attention techniques to merge infrared and visible images seamlessly. By utilizing three distinct fields for feature extraction, the model enhances the accuracy of data fusion. Moreover, the deep attention mechanism facilitates the extraction of multi-level features through spatial and channel attention, thus enabling effective data fusion. Reference [29] in their work, have introduced a versatile fusion model called multi-exposure image fusion on generative adversarial networks (MEF-GAN) with the aim of effectively fusing image data. The proposed model consists of two components: a generator and a discriminator network, which are trained concurrently to form an adversarial network. The generator is responsible for producing synthesized fused images that resemble the source image, while the discriminator is trained to differentiate between the source image and the fake fused images generated by the generator. This adversarial relationship helps to preserve data integrity and prevents information loss in the fused image, ultimately leading to a fused image probability distribution that closely approximates reality. In their recent study, reference [30] proposed a robust fusion model called correlation driven feature decomposition fusion (CDDFuse) as an effective solution. The authors employed Restormer blocks to extract cross-modality image features and seamlessly integrate them with an advanced convolutional neural network (CNN) model.