A survey of deep learning techniques for autonomous driving

• Published in: Journal of field robotics Vol. 37; no. 3; pp. 362 - 386
• Main Authors: Grigorescu, Sorin, Trasnea, Bogdan, Cocias, Tiberiu, Macesanu, Gigel
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.04.2020
• Subjects: AI for self‐driving vehicles; Algorithms; Arbitration; Artificial intelligence; autonomous driving; Control algorithms; Deep learning; deep learning for autonomous driving; Machine learning; Motion control; Path planning; Perception; Recurrent neural networks; Steering
• ISSN: 1556-4959; 1556-4967
• DOI: 10.1002/rob.21918

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 (AI). 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 gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.