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008			230112s2022 sz | s |||| 0|eng d
020			_a9783031012334
024	7		_a10.1007/978-3-031-01233-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTL152.8 _b2022 EB
245	0	0	_aDeep Neural Networks and Data for Automated Driving : _bRobustness, Uncertainty Quantification, and Insights Towards Safety _cedited by Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XVIII, 427 páginas) _b117 ilustraciones, 103 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
505	0		_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.
506	0		_aOpen Access
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.
988			_aSpringer_Engineering_2022
650		7	_2embne _9673285 _aAutomóviles _xControl automático
650		7	_2embne _9678664 _aRedes neuronales artificiales
700	1		_aFingscheidt, Tim _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aGottschalk, Hanno _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700	1		_aHouben, Sebastian _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
776	0	8	_iPrinted edition: _z9783031012327
776	0	8	_iPrinted edition: _z9783031012341
776	0	8	_iPrinted edition: _z9783031012358
776	0	8	_iPrinted edition: _z9783031034916
776	0	8	_iPrinted edition: _z9783031034909
776	0	8	_iPrinted edition: _z9783031034893
776	0	8	_iPrinted edition: _z9783031034886
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01233-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eIG _zSI