A survey of deep learning techniques for autonomous driving.
In: Journal of Field Robotics, Jg. 37 (2020-04-01), Heft 3, S. 362-386
academicJournal
Zugriff:
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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Field Robotics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Titel: |
A survey of deep learning techniques for autonomous driving.
|
---|---|
Autor/in / Beteiligte Person: | Grigorescu, Sorin ; Trasnea, Bogdan ; Cocias, Tiberiu ; Macesanu, Gigel |
Zeitschrift: | Journal of Field Robotics, Jg. 37 (2020-04-01), Heft 3, S. 362-386 |
Veröffentlichung: | 2020 |
Medientyp: | academicJournal |
ISSN: | 1556-4959 (print) |
DOI: | 10.1002/rob.21918 |
Schlagwort: |
|
Sonstiges: |
|