Deep Neural Networks and Data for Automated Driving Robustness, Uncertainty Quantification, and Insights Towards Safety

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 testi...

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Hlavní autoři: Fingscheidt, Tim, Gottschalk, Hanno, Houben, Sebastian
Médium: E-kniha
Jazyk:angličtina
Vydáno: Cham Springer Nature 2022
Springer International Publishing AG
University of Wuppertal
Vydání:1
Témata:
Autonomous Driving
Careers
Deep Learning
Engineering
Environment Perception
Highly Automated Driving
Nonfiction
Safety
Technology
ISBN:3031034899, 303101233X, 9783031012334, 9783031034893, 9783031012327, 3031012321
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Abstract 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.
AbstractList 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.
Author Houben, Sebastian
Gottschalk, Hanno
Fingscheidt, Tim
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SubjectTerms Autonomous Driving
Careers
Deep Learning
Engineering
Environment Perception
Highly Automated Driving
Nonfiction
Safety
Technology
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Engineering.
Nonfiction.
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Subtitle Robustness, Uncertainty Quantification, and Insights Towards Safety
Title Deep Neural Networks and Data for Automated Driving
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