Generative Adversarial Networks (GANs) are increasingly becoming essential tools for improving the perception of autonomous vehicles in adverse environmental conditions, like low light, fog, rain, and snow. This survey discusses recent trends in applying GANs to tackle these problems and classifies the available work based on environmental conditions and GAN architectures. Studies showcase the impact of different GAN models such as CycleGAN, Super-Resolution GANs, and DeblurGAN in enhancing image quality, visibility, and detection accuracy of objects under challenging conditions. For instance, CycleGAN has been remarkably effective in nighttime image enhancement; DeblurGANs significantly reduce motion blur at capture. Furthermore, multi-modal fusion approaches have been applied very successfully with RGB and infrared (IR) data creating strong perception systems that work well in diverse operational conditions. However, practically deployed driving solutions must process information quickly while remaining computationally efficient. This survey emphasizes how GAN technology has made autonomous vehicle research more adaptable and safer; it also points out potential graduate study directions involving lighter-weight models of GANs or domain-adaptive architectures to make them more applicable. We hope our comprehensive review will help researchers utilize GAN technology to enhance perception for autonomous vehicles.