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A diverse set of methods have been devised to develop autonomous driving platforms. They range from modular systems, systems that perform manual decomposition of the problem, systems where the components are optimized independently, and a large number of rules are programmed manually, to end-to-end deep-learning frameworks. Today’s systems rely on a subset of the following: camera images, HD maps, inertial measurement units, wheel encoders, and active 3D sensors (LIDAR, radar). There is a general agreement that much of the self-driving software stack will continue to incorporate some form of machine learning in any of the above mentioned systems in the future. Self-driving cars present one of today’s greatest challenges and opportunities for Artificial Intelligence (AI). Despite substantial investments, existing methods for building autonomous vehicles have not yet succeeded, i.e., there are no driverless cars on public roads today without human safety drivers. Nevertheless, a few groups have started working on extending the idea of learned tasks to larger functions of autonomous driving. Initial results on learned road following are very promising. The goal of this workshop is to explore ways to create a framework that is capable of learning autonomous driving capabilities beyond road following, towards fully driverless cars. The workshop will consider the current state of learning applied to autonomous vehicles and will explore how learning may be used in future systems. The workshop will span both theoretical frameworks and practical issues especially in the area of deep learning.