ISBN: 9783031012327 - deep neural networks and data for automated driving: robustness, uncertainty quantification, and insights towards safety (16 risultati)

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.…

Hardcover. Condizione: Brand New. 445 pages. 9.25x6.10x9.21 inches. In Stock.

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Condizione: new. Questo è un articolo print on demand.

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 448 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents the latest developments from industry and research on automated driving and artificial intelligenceProvides in introduction to current knowledge in neural networks and AIProvides a basis for future research and a guide for practiti.…

Condizione: New. Print on Demand.

Condizione: New. PRINT ON DEMAND.

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch.…