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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: R1,564
Discovery Miles 15 640
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Deep Neural Networks and Data for Automated Driving - Robustness, Uncertainty Quantification, and Insights Towards Safety (Hardcover, 1st ed. 2022)
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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.
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