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End-to-end deep learning-based autonomous driving control for high-speed environment.
- Source :
- Journal of Supercomputing; Feb2022, Vol. 78 Issue 2, p1961-1982, 22p
- Publication Year :
- 2022
-
Abstract
- With the recent emergence of artificial intelligence (AI) technology, autonomous vehicle industry has rapidly adopted this technology to investigate self-driving systems based on AI technology. Although autonomous driving is frequently used in high-speed environments, most studies are conducted on low-speed driving on complex urban roads. Currently, most commercialized self-driving cars in SAE autonomous driving level 2 provide practical performance on high-speed roads using various sensors. However, these systems have to process huge sensor data and apply complex control algorithms. Recently, studies have been conducted on the use of image-based end-to-end deep learning to control autonomous driving systems that can be configured at a low cost without expensive sensors and complex processes. In this study, we proposed an autonomous driving control system using a novel end-to-end deep learning model for high-speed environments, and also compared the performance of the proposed system with NVIDIA end-to-end driving system. [ABSTRACT FROM AUTHOR]
- Subjects :
- DRIVERLESS cars
AUTONOMOUS vehicles
DEEP learning
ARTIFICIAL intelligence
Subjects
Details
- Language :
- English
- ISSN :
- 09208542
- Volume :
- 78
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Journal of Supercomputing
- Publication Type :
- Academic Journal
- Accession number :
- 154873484
- Full Text :
- https://doi.org/10.1007/s11227-021-03929-8