Most wireless communication technologies are built on top of a very sophisticated theoretical framework whose foundations were laid more than seventy years ago by Claude Shannon. Although these theories are now the main pillars of telecommunications, they exhibit inherent limitations in their practical use. The difficulty of optimizing very complex equations in real-time, the high-dimensional nature of wireless applications, and the dependency on empirical data for creating models make wireless communications a perfect candidate for utilizing deep learning. While deep learning technologies have been successfully incorporated into countless familiar applications, their use in wireless communications is relatively new but has already demonstrated benefits. This talk will start with a discussion of the challenges of wireless communication, generally and in the context of autonomous vehicles, followed by a summary of deep learning methodologies and the most relevant artificial neural networks. Next, we will thoroughly discuss various applications of deep learning to address some of the most difficult problems in wireless communications. Furthermore, we will explore some of the proposed solutions that address the shortcomings of current communication systems used in autonomous mobility (e.g., urban air mobility, industrial drones, last-mile delivery, self-driving cars). We will conclude by presenting some open research questions whose answers may pave the way to the coming breakthroughs in wireless communication technologies.