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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Eará dahkkit:	Fingscheidt, Tim (Doaimmaheaddji), Gottschalk, Hanno (Doaimmaheaddji), Houben, Sebastian (Doaimmaheaddji)
Materiálatiipa:	Elektrovnnalaš Girjji oassi
Giella:	eaŋgalasgiella
Almmustuhtton:	Cham Springer Nature 2022
Fáttát:	Automotive technology & trades Mathematical modelling Image processing Databases Highly Automated Driving Autonomous Driving Environment Perception Deep Learning Safety
Liŋkkat:	OAPEN Library: download the publication OAPEN Library: description of the publication
Fáddágilkorat:	Lasit fáddágilkoriid Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!

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

Almmustuhtton 2022

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Elektrovnnalaš E-girji

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

Almmustuhtton 2022

DOAB: download the publication
DOAB: description of the publication

Elektrovnnalaš Girjji oassi

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

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

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

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