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...
Saved in:
: | |
---|---|
TeilnehmendeR: | |
Place / Publishing House: | Cham : : Springer International Publishing AG,, 2022. ©2022. |
Year of Publication: | 2022 |
Language: | English |
Physical Description: | 1 online resource (435 pages) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
LEADER | 02502nam a22003733i 4500 | ||
---|---|---|---|
001 | 993562294704498 | ||
005 | 20221003232355.0 | ||
006 | m o d | | ||
007 | cr#cnu|||||||| | ||
008 | 220919s2022 sz fo 000|0 eng d | ||
020 | |a 3-031-01233-X | ||
035 | |a (CKB)5860000000053521 | ||
035 | |a (MiAaPQ)EBC7018938 | ||
035 | |a (Au-PeEL)EBL7018938 | ||
035 | |a (OCoLC)1331559221 | ||
035 | |a (EXLCZ)995860000000053521 | ||
040 | |a MiAaPQ |b eng |e rda |e pn |c MiAaPQ |d MiAaPQ | ||
050 | 4 | |a TL1-483 | |
100 | 1 | |a Fingscheidt, Tim, |d 1966-. | |
245 | 1 | 0 | |a Deep neural networks and data for automated driving : |b robustness, uncertainty quantification, and insights towards safety / |c Tim Fingscheidt [et al.] |
264 | 1 | |a Cham : |b Springer International Publishing AG, |c 2022. | |
264 | 4 | |c ©2022. | |
300 | |a 1 online resource (435 pages) | ||
336 | |a text |b txt |2 rdacontent | ||
337 | |a computer |b c |2 rdamedia | ||
338 | |a online resource |b cr |2 rdacarrier | ||
520 | |a 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. | ||
588 | |a Description based on publisher supplied metadata and other sources. | ||
650 | 0 | |a Automated vehicles. | |
650 | 0 | |a Deep learning (Machine learning). | |
776 | |z 3-031-01232-1 | ||
700 | 1 | |a Gottschalk, Hanno. | |
700 | 1 | |a Houben, Sebastian. | |
906 | |a BOOK | ||
ADM | |b 2022-12-22 20:50:51 Europe/Vienna |d 00 |f system |c marc21 |a 2022-07-02 22:45:44 Europe/Vienna |g false | ||
AVE | |P DOAB Directory of Open Access Books |x https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338970690004498&Force_direct=true |Z 5338970690004498 |8 5338970690004498 |