Téléchargez le livre : Deep Neural Networks and Data for Automated Driving

Deep Neural Networks and Data for Automated Driving

Robustness, Uncertainty Quantification, and Insights Towards Safety

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Springer


Paru le : 2022-06-17



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Description

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 testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety?This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and,last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
Pages
427 pages
Collection
n.c
Parution
2022-06-17
Marque
Springer
EAN papier
9783031012327
EAN PDF
9783031012334
Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
42
Taille du fichier
12319 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
0,00 €
EAN EPUB
9783031012334
Informations sur l'ebook
Nombre pages copiables
4
Nombre pages imprimables
42
Taille du fichier
67758 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
0,00 €

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