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Deep Neural Networks and Data for Automated Driving - Robustness, Uncertainty Quantification, and Insights Towards Safety (Hardcover, 1st ed. 2022) Loot Price: R1,601
Discovery Miles 16 010
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 (Hardcover, 1st ed. 2022)

Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben

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Loot Price R1,601 Discovery Miles 16 010 | Repayment Terms: R150 pm x 12*

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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 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.

General

Imprint: Springer International Publishing AG
Country of origin: Switzerland
Release date: June 2022
First published: 2022
Editors: Tim Fingscheidt • Hanno Gottschalk • Sebastian Houben
Dimensions: 235 x 155mm (L x W)
Format: Hardcover
Pages: 427
Edition: 1st ed. 2022
ISBN-13: 978-3-03-101232-7
Categories: Books > Professional & Technical > Transport technology > Intelligent & automated transport system technology
Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling
Books > Professional & Technical > Transport technology > Automotive technology > General
Books > Computing & IT > Applications of computing > Databases > General
Books > Computing & IT > Applications of computing > Image processing > General
LSN: 3-03-101232-1
Barcode: 9783031012327

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