The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. It has been widely used in various fields, such as end-to-end control, robotic control, recommendation systems, and natural language dialogue systems. Since a full description on all deep learning algorithms used in autonomous vehicles would be out of the scope of this manuscript, we refer the interested reader to the insightful texts on this topic in [59, 128, 96, 163, 178, 7, 101]. The main contributions of this paper: 1) presenting a survey of the recent advances of deep reinforcement learning and 2) introducing a framework for end-end autonomous driving using deep reinforcement learning to the automotive community. The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. Deep reinforcement learning (RL) has become one of the most popular topics in artificial intelligence research. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. We investigate the major fields of self-driving … Voyage Deep Drive is a simulation platform released last month where you can build reinforcement learning algorithms in a realistic simulation. A brief summary on learning strategies, datasets, and tools for deep learning in autonomous vehicles is given. Lately, I have noticed a lot of development platforms for reinforcement learning in self-driving cars. A Survey of Deep Learning Techniques for Autonomous Driving Sorin Grigorescu ... as well as the deep reinforcement learning paradigm. This is a survey of autonomous driving technologies with deep learning methods. With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. Reinforcement learning (RL) has distinguished itself as a prominent learning method to augment the efficacy of autonomous systems. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. The rest of the paper is divided into two parts. time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. Recent advances in deep learning studies have complemented existing RL methods and led to a crucial breakthrough in the We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. It looks similar to CARLA.. 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