We take a look under the hood to discover how deep learning works, and then review their current and future impact on automobiles. This talk describes what deep networks are, how different architectures tackle the diverse needs for driving, and how these computationally expensive operations are optimized for deployment. Examples are given demonstrating their widespread usage in the auto sector, latest research trends, the fusion of many types of sensors, and the usage of various software and hardware technologies.