  The last decade witnessed increasingly rapid progress in self-driving vehicle
technology, mainly backed up by advances in the area of deep learning and
artificial intelligence. The objective of this paper is to survey the current
state-of-the-art on deep learning technologies used in autonomous driving. We
start by presenting AI-based self-driving architectures, convolutional and
recurrent neural networks, as well as the deep reinforcement learning paradigm.
These methodologies form a base for the surveyed driving scene perception, path
planning, behavior arbitration and motion control algorithms. We investigate
both the modular perception-planning-action pipeline, where each module is
built using deep learning methods, as well as End2End systems, which directly
map sensory information to steering commands. Additionally, we tackle current
challenges encountered in designing AI architectures for autonomous driving,
such as their safety, training data sources and computational hardware. The
comparison presented in this survey helps to gain insight into the strengths
and limitations of deep learning and AI approaches for autonomous driving and
assist with design choices
