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Deep Neural Networks And Data For Automated Driving Robustness Uncertainty Quantification And Insights Towards Safety Tim Fingscheidt

  • SKU: BELL-44746142
Deep Neural Networks And Data For Automated Driving Robustness Uncertainty Quantification And Insights Towards Safety Tim Fingscheidt
$ 31.00 $ 45.00 (-31%)

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Deep Neural Networks And Data For Automated Driving Robustness Uncertainty Quantification And Insights Towards Safety Tim Fingscheidt instant download after payment.

Publisher: Springer
File Extension: PDF
File size: 11.87 MB
Pages: 445
Author: Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben
ISBN: 9783031012327, 3031012321
Language: English
Year: 2022

Product desciption

Deep Neural Networks And Data For Automated Driving Robustness Uncertainty Quantification And Insights Towards Safety Tim Fingscheidt by Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben 9783031012327, 3031012321 instant download after payment.

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.

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