EdgeNet: An End-to-End Deep Neural Network Pretrained with Synthetic Data for a Real-World Autonomous Driving Application

EdgeNet: An End-to-End Deep Neural Network Pretrained with Synthetic Data for a Real-World Autonomous Driving Application

This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the...

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Bibliographic Details
Main Authors: Leanne Miller, Pedro J. Navarro, Francisca Rosique
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
end-to-end architectures
multimodal synthetic dataset
autonomous driving
Online Access:https://www.mdpi.com/1424-8220/25/1/89
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