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Deep Neural Networks and Data for Automated Driving

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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 implausible
predictions to directed attacks by means of malicious inputs. Cyber-physical systems
employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help
to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a
structured and broad overview of them

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Availability

Detail Information

Series Title

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Call Number

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Publisher

: Springer Cham., 2022

Collation

XVIII, 427

Language

English

ISBN/ISSN

9783031012334

Classification

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Content Type

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Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Deep Neural Networks
Data for Automated Driving

Specific Detail Info

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Statement of Responsibility

Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben

Other Information

Cataloger

angga

Source

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Validator

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Digital Object Identifier (DOI)

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

Journal Volume

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Journal Issue

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Subtitle

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