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001431438 001__ 1431438
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001431438 020__ $$a9783031012334$$q(electronic bk.)
001431438 020__ $$a303101233X$$q(electronic bk.)
001431438 020__ $$z9783031012327$$q(print)
001431438 0247_ $$a10.1007/978-3-031-01233-4$$2doi
001431438 035__ $$aSP(OCoLC)1331559221
001431438 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dOCLCF$$dINU$$dOCLCQ
001431438 049__ $$aISEA
001431438 050_4 $$aTL152.8
001431438 08204 $$a629.04/6$$223/eng/20220622
001431438 24500 $$aDeep neural networks and data for automated driving :$$brobustness, uncertainty quantification, and insights towards safety /$$cTim Fingscheidt, Hanno Gottschalk, Sebastian Houben, editors.
001431438 264_1 $$aCham, Switzerland :$$bSpringer,$$c2022.
001431438 300__ $$a1 online resource (xviii, 427 pages) :$$b117 illustrations (some color)
001431438 336__ $$atext$$btxt$$2rdacontent
001431438 337__ $$acomputer$$bc$$2rdamedia
001431438 338__ $$aonline resource$$bcr$$2rdacarrier
001431438 5050_ $$aChapter 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.
001431438 5060_ $$aOpen access.$$5GW5XE
001431438 520__ $$aThis open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
001431438 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed June 22, 2022).
001431438 650_0 $$aAutomated vehicles.
001431438 650_0 $$aVehicular ad hoc networks (Computer networks)
001431438 650_0 $$aNeural networks (Computer science)
001431438 655_0 $$aElectronic books.
001431438 7001_ $$aFingscheidt, Tim,$$d1966-$$eeditor.
001431438 7001_ $$aGottschalk, Hanno,$$d1967-$$eeditor.
001431438 7001_ $$aHouben, Sebastian,$$d1983-$$eeditor.
001431438 852__ $$bebk
001431438 85640 $$3Springer Nature$$uhttps://link.springer.com/10.1007/978-3-031-01233-4$$zOnline Access$$91397441.2
001431438 909CO $$ooai:library.usi.edu:1431438$$pGLOBAL_SET
001431438 980__ $$aBIB
001431438 980__ $$aEBOOK
001431438 982__ $$aEbook
001431438 983__ $$aOnline
001431438 994__ $$a92$$bISE