Presents the latest developments from industry and research on automated driving and artificial intelligence
Provides in introduction to current knowledge in neural networks and AI
Provides a basis for future research and a guide for practitioners in industry
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Table of contents (15 chapters)
Safe AI—An Overview
Recent Advances in Safe AI for Automated Driving
About this book
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.
Editors and Affiliations
Institute for Communications Technology, Technische Universität Braunschweig, Braunschweig, Germany
Fachgruppe Mathematik und Informatik, Bergische Universität Wuppertal, Wuppertal, Germany
Schloss Birlinghoven, Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Sankt Augustin, Germany
About the editors
Hanno Gottschalk studied Physics and Mathematics and received diploma degrees from the Ruhr University Bochum in 1995 and 1997, respectively. After finishing his Ph.D. on Mathematical Physics in 1999, he joined the University La Sapienza of Rome for a PostDoc year, before continuing his academic career as PostDoc at Bonn University, where he habilitated in mathematics in 2003. Since 2005, he was lecturer (C2) at the University of Bonn and joined Siemens Energy from 2007–2011 as a Core Competency Owner for probabilistic design. Since 2011, he is Professor for stochastics at the University of Wuppertal. In 2018, he became co-founding Director of the Interdisciplinary Center for Machine Learning and Data Analytics (IZMD) of the University of Wuppertal. His research in the field of deep learning is focused on uncertainty and safety for deep learning perception algorithms. Applications lie in the field of false positive and false negative prediction and detection and retrieval of out of distribution objects. Apart from bi-lateral work with Volkswagen and Aptiv, he is member of the AI Validation, AI Delta Learning, and AI Data Tooling consortia within the AI family of large-scale projects. Hanno Gottschalk brings his special knowledge as statistician and mathematician to the field of automated driving and combines this with cutting edge technology in deep learning.
Sebastian Houben studied Mathematics and Computer Science at the University in Hagen and graduated in 2009. He pursued Ph.D. studies at the Ruhr University of Bochum graduating with distinction in 2015. After his postdoctoral studies at the University of Bonn, he was appointed Junior Professor for Applied Computer Science at the Ruhr University of Bochum where he headed the Group of Real-time Computer Vision. As of early 2020, he is a senior researcher with the Fraunhofer Institute for Intelligent Analysis and Information Systems. His research interests cover computer vision and environment perception in autonomous robotics, in particular in the field of automated driving. Within the consortium KI-Absicherung and the competency center Machine-Learning-Rhein-Ruhr (ML2R), he represents the topic Trustworthy AI and is particularly interested in practical methods for explainability of black-box models, uncertainty estimation in neural networks, and visual analytics. Sebastian Houben believes that artificial intelligence would be an even stronger technology if it was simpler, more robust, and safer to use. His role at Fraunhofer allows him to accompany this transfer from the research laboratories into practical applications.
Book Title: Deep Neural Networks and Data for Automated Driving
Book Subtitle: Robustness, Uncertainty Quantification, and Insights Towards Safety
Editors: Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben
Publisher: Springer Cham
Copyright Information: The Editor(s) (if applicable) and The Author(s) 2022
License: CC BY
Hardcover ISBN: 978-3-031-01232-7Published: 18 June 2022
Softcover ISBN: 978-3-031-01235-8Published: 18 June 2022
eBook ISBN: 978-3-031-01233-4Published: 17 June 2022
Edition Number: 1
Number of Pages: XVIII, 427
Number of Illustrations: 14 b/w illustrations, 103 illustrations in colour