Deep Neural Networks and Data for Automated Driving Robustness, Uncertainty Quantification, and Insights Towards Safety

Deep Neural Networks and Data for Automated Driving Robustness, Uncertainty Quantification, and Insights Towards Safety

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This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testi...

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Bibliographic Details
Other Authors: Fingscheidt, Tim (Editor), Gottschalk, Hanno (Editor), Houben, Sebastian (Editor)
Format: Electronic Book Chapter
Language:English
Published: Cham Springer Nature 2022
Subjects:
Automotive technology & trades
Mathematical modelling
Image processing
Databases
Highly Automated Driving
Autonomous Driving
Environment Perception
Deep Learning
Safety
Online Access:DOAB: download the publication
DOAB: description of the publication
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