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Deep Neural Networks and Data for Automated Driving

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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?

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Detail Information

Series Title

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Call Number

XVIII, 427

Publisher

Springer Cham : Springer Cham., 2022

Collation

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Language

English

ISBN/ISSN

978-3-031-01233-4

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

1

Subject(s)

Automotive Engineering
Data Engineering

Specific Detail Info

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Statement of Responsibility

Tim Fingscheidt

Other Information

Cataloger

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Source

https://link.springer.com/book/10.1007/978-3-031-01233-4

Validator

Suwardi

Digital Object Identifier (DOI)

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Journal Volume

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Journal Issue

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Subtitle

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