Autonomous driving represents a significant advancement in the transportation industry, enhancing vehicle intelligence, optimizing traffic management, and improving user experiences. Central to these innovations is deep learning, which enables systems to handle complex data and make informed decisions. Our survey explores critical applications of deep learning in autonomous driving, such as perception and detection, localization and mapping, and decision-making and control. We investigate specialized deep learning techniques, including convolutional neural networks, recurrent neural networks, self-attention transformers, and their variants, among others. These methods are applied within various learning paradigms—supervised, unsupervised, and reinforcement learning—to suit the specific needs of autonomous driving. Our analysis evaluates the effectiveness, benefits, and limitations of these technologies, focusing on their integration with other intelligent algorithms to enhance system performance. Furthermore, we examine the architectures of autonomous systems, analyzing how knowledge and information are organized from modular, pipeline-based frameworks to comprehensive end-to-end models. By presenting an exhaustive overview of the progressing domain of autonomous driving and bridging various research areas, our survey aims to synthesize diverse research threads into a unified narrative. This effort not only aims to enhance our understanding but also pushes the boundaries of what is achievable in this interdisciplinary field.