The field of autonomous driving is rapidly evolving, with a focus on developing scalable and affordable solutions. Researchers are exploring the use of low-cost, commercially available edge devices such as dash-cams and open-source software to make autonomous driving technology more accessible. Another area of innovation is the application of generative AI models, including diffusion models and large language models, to improve the safety and efficiency of autonomous vehicles. These models are being used for tasks such as trajectory planning, object detection, and scene understanding. Noteworthy papers in this area include: AI-CDA4All, which presents a novel approach to democratizing cooperative driving automation technology, Unsupervised Raindrop Removal from a Single Image using Conditional Diffusion Models, which introduces a new technique for removing raindrops from images using diffusion-based image inpainting, Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix, which leverages the diffusion model Instruct Pix2Pix to develop prompting methodologies for generating realistic datasets with weather-based augmentations, and Provably safe and human-like car-following behaviors: Part 2, which introduces a novel multi-phase projection-based car-following model that balances safety and performance by incorporating bounded acceleration and deceleration rates while emulating key human driving principles.