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Found 4 from your keywords: author="Fingscheidt"
cover
Deep Neural Networks and Data for Automated Driving
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Tim FingscheidtHanno GottschalkSebastian Houben

Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and impl…

Edition
-
ISBN/ISSN
9783031012334
Collation
XVIII, 427
Series Title
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Call Number
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cover
Foundations of Robotics
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Damith HerathDavid St-OngeFingscheidt

This chapter is an extensive overview of the history of automata and robotics from the Hellenistic period, which saw the birth of science and technology, and during which lived the founders of modern engineering, to today. Contemporary robotics is actually a very young field. It was preceded by a 2000-years period in which highly sophisticated automata were built for very different purposes…

Edition
-
ISBN/ISSN
9789811919831
Collation
XXI, 543
Series Title
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Call Number
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cover
Deep Neural Networks and Data for Automated Driving
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Tim Fingscheidt

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 robustne…

Edition
1
ISBN/ISSN
978-3-031-01233-4
Collation
-
Series Title
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Call Number
XVIII, 427
cover
Deep Neural Networks and Data for Automated Driving
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Fingscheidt,,TimHouben, Sebastian

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 robustne…

Edition
-
ISBN/ISSN
978-3-031-01233-4
Collation
-
Series Title
-
Call Number
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