Deep neural networks and data for automated driving : : robustness, uncertainty quantification, and insights towards safety / / Tim Fingscheidt [et al.]
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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Place / Publishing House: | Cham : : Springer International Publishing AG,, 2022. ©2022. |
Year of Publication: | 2022 |
Language: | English |
Physical Description: | 1 online resource (435 pages) |
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Fingscheidt, Tim, 1966-. Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / Tim Fingscheidt [et al.] Cham : Springer International Publishing AG, 2022. ©2022. 1 online resource (435 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier 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? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above. Description based on publisher supplied metadata and other sources. Automated vehicles. Deep learning (Machine learning). 3-031-01232-1 Gottschalk, Hanno. Houben, Sebastian. |
language |
English |
format |
eBook |
author |
Fingscheidt, Tim, 1966-. |
spellingShingle |
Fingscheidt, Tim, 1966-. Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / |
author_facet |
Fingscheidt, Tim, 1966-. Gottschalk, Hanno. Houben, Sebastian. |
author_variant |
t f tf |
author2 |
Gottschalk, Hanno. Houben, Sebastian. |
author2_variant |
h g hg s h sh |
author2_role |
TeilnehmendeR TeilnehmendeR |
author_sort |
Fingscheidt, Tim, 1966-. |
title |
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / |
title_sub |
robustness, uncertainty quantification, and insights towards safety / |
title_full |
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / Tim Fingscheidt [et al.] |
title_fullStr |
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / Tim Fingscheidt [et al.] |
title_full_unstemmed |
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / Tim Fingscheidt [et al.] |
title_auth |
Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / |
title_new |
Deep neural networks and data for automated driving : |
title_sort |
deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / |
publisher |
Springer International Publishing AG, |
publishDate |
2022 |
physical |
1 online resource (435 pages) |
isbn |
3-031-01233-X 3-031-01232-1 |
callnumber-first |
T - Technology |
callnumber-subject |
TL - Motor Vehicles and Aeronautics |
callnumber-label |
TL1-483 |
callnumber-sort |
TL 11 3483 |
illustrated |
Not Illustrated |
oclc_num |
1331559221 |
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(CKB)5860000000053521 (MiAaPQ)EBC7018938 (Au-PeEL)EBL7018938 (OCoLC)1331559221 (EXLCZ)995860000000053521 |
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Deep neural networks and data for automated driving : robustness, uncertainty quantification, and insights towards safety / |
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