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

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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 testi...

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Ente Autore: SpringerLink (Online service)
Altri autori: Fingscheidt, Tim (Redattore), Gottschalk, Hanno (Redattore), Houben, Sebastian (Redattore)
Natura: Elettronico eBook
Lingua:inglese
Pubblicazione: Cham : Springer International Publishing : Imprint: Springer, 2022.
Edizione:1st ed. 2022.
Soggetti:
Automotive engineering.
Neural networks (Computer science) .
Computer vision.
Engineering > Data processing.
Automotive Engineering.
Mathematical Models of Cognitive Processes and Neural Networks.
Computer Vision.
Data Engineering.
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