End-to-End Deep Neural Network Architectures for Speed and Steering Wheel Angle Prediction in Autonomous Driving

End-to-End Deep Neural Network Architectures for Speed and Steering Wheel Angle Prediction in Autonomous Driving

The complex decision-making systems used for autonomous vehicles or advanced driver-assistance systems (ADAS) are being replaced by end-to-end (e2e) architectures based on deep-neural-networks (DNN). DNNs can learn complex driving actions from datasets containing thousands of images and data obtaine...

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Bibliographic Details
Main Authors: Pedro J. Navarro, Leanne Miller, Francisca Rosique, Carlos Fernández-Isla, Alberto Gila-Navarro
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Electronics
Subjects:
autonomous driving
end-to-end architecture
speed and steering wheel angle prediction
DNN for regression
Online Access:https://www.mdpi.com/2079-9292/10/11/1266

Internet

https://www.mdpi.com/2079-9292/10/11/1266

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