Deep Neural Networks and Data for Automated Driving (Record no. 200457320)
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| fixed length control field | 04837nam a22005775i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | TR-AnTOB |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20231116091127.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220617s2022 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783031012334 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-3-031-01233-4 |
| Source of number or code | doi |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | TR-AnTOB |
| Language of cataloging | eng |
| Description conventions | rda |
| Transcribing agency | TR-AnTOB |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | İngilizce |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | TL152.8 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | TRC |
| Source | bicssc |
| Subject category code | TEC009090 |
| Source | bisacsh |
| Subject category code | TRC |
| Source | thema |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) | |
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) | TL152.8EBK |
| 245 10 - TITLE STATEMENT | |
| Title | Deep Neural Networks and Data for Automated Driving |
| Medium | [electronic resource] : |
| Remainder of title | Robustness, Uncertainty Quantification, and Insights Towards Safety / |
| Statement of responsibility, etc. | edited by Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2022. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Cham : |
| Name of producer, publisher, distributor, manufacturer | Springer International Publishing : |
| -- | Imprint: Springer, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 347 ## - DIGITAL FILE CHARACTERISTICS | |
| File type | text file |
| Encoding format | |
| Source | rda |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Chapter 1. Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety -- Chapter 2. Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance? -- Chapter 3. Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces -- Chapter 4. Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation -- Chapter 5. Improved DNN Robustness by Multi-Task Training With an Auxiliary Self-Supervised Task -- Chapter 6. Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation -- Chapter 7. Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations -- Chapter 8. Confidence Calibration for Object Detection and Segmentation -- Chapter 9. Uncertainty Quantification for Object Detection: Output- and Gradient-based Approaches -- Chapter 10. Detecting and Learning the Unknown in Semantic Segmentation -- Chapter 11. Evaluating Mixture-of-Expert Architectures for Network Aggregation -- Chapter 12. Safety Assurance of Machine Learning for Perception Functions -- Chapter 13. A Variational Deep Synthesis Approach for Perception Validation -- Chapter 14. The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique -- Chapter 15. Joint Optimization for DNN Model Compression and Corruption Robustness. |
| 506 0# - RESTRICTIONS ON ACCESS NOTE | |
| Terms governing access | Open Access |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Automotive engineering. |
| Topical term or geographic name entry element | Neural networks (Computer science) . |
| Topical term or geographic name entry element | Computer vision. |
| Topical term or geographic name entry element | Engineering—Data processing. |
| Topical term or geographic name entry element | Automotive Engineering. |
| Topical term or geographic name entry element | Mathematical Models of Cognitive Processes and Neural Networks. |
| Topical term or geographic name entry element | Computer Vision. |
| Topical term or geographic name entry element | Data Engineering. |
| 653 #0 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | Automated vehicles |
| Uncontrolled term | Vehicular ad hoc networks (Computer networks) |
| Uncontrolled term | Neural networks (Computer science) |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Fingscheidt, Tim. |
| Relator term | editor. |
| Relator code | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| Personal name | Gottschalk, Hanno. |
| Relator term | editor. |
| Relator code | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| Personal name | Houben, Sebastian. |
| Relator term | editor. |
| Relator code | edt |
| -- | http://id.loc.gov/vocabulary/relators/edt |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-3-031-01233-4">https://doi.org/10.1007/978-3-031-01233-4</a> |
| Materials specified | Springer eBooks |
| Public note | Online access link to the resource |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Library of Congress Classification |
| Koha item type | E-Book |
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