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.
Tim Fingscheidt received the Dipl.-Ing. degree in Electrical Engineering in 1993 and the Ph.D. degree in 1998 from RWTH Aachen University, Germany, both with distinction. He joined AT&T Labs, Florham Park, NJ, USA, for a PostDoc in 1998 and Siemens AG (Mobile Devices), Munich, Germany, in 1999, heading a signal processing development team. After a stay with Siemens Corporate Technology, Munich, Germany, from 2005 to 2006, he became Full Professor with the Institute for Communications Technology, Technische Universität (TU) Braunschweig, Germany, holding the Chair of "Signal Processing and Machine Learning". His research interests are machine learning in vision and time series such as speech, with focus on environment perception, signal classification, coding, and enhancement. He is founder of the TU Braunschweig Deep Learning Lab (tubs.DLL), a graduate student research thinks tank being active in publicly funded and industry research projects. Many of his projects have been dealing with automotive applications. Since 2018, he has been actively involved in the large-scale national research projects AI Platform Concept, AI Validation, AI Delta Learning, and AI Data Tooling, contributing research in robust semantic segmentation, monocular depth estimation, domain adaptation, corner case detection, and learned image coding. Prof. Fingscheidt received numerous national and international awards for his publications; among these, three CVPR workshop best paper awards in 2019, 2020, and 2021. He is interested in where academia meets industry and where machine learning meets highly automated driving.
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.
蒂姆·芬克謝德（Tim Fingscheidt）於1993年獲得德國亞琛工業大學（RWTH Aachen University）電機工程的Dipl.-Ing.學位，並於1998年獲得博士學位，兩者均以優異成績畢業。他於1998年加入美國新澤西州佛羅漢公園的AT&T實驗室擔任博士後研究員，並於1999年加入德國慕尼黑的西門子公司（Mobile Devices），負責一個信號處理開發團隊。2005年至2006年間，他在德國慕尼黑的西門子企業技術部工作，隨後成為德國布倫瑞克工業大學（Technische Universität Braunschweig）通訊技術研究所的正教授，擔任「信號處理與機器學習」的講座教授。他的研究興趣包括視覺和時間序列（如語音）中的機器學習，重點在於環境感知、信號分類、編碼和增強。他是布倫瑞克工業大學深度學習實驗室（tubs.DLL）的創始人，該實驗室是一個活躍於公共資助和產業研究項目的研究生學生智庫。他的許多項目涉及汽車應用。自2018年以來，他積極參與大型國家研究項目，包括AI平台概念、AI驗證、AI Delta學習和AI數據工具，貢獻於穩健的語義分割、單目深度估計、領域適應、邊緣案例檢測和學習圖像編碼等研究。芬克謝德教授因其出版物獲得了眾多國內外獎項，其中包括2019年、2020年和2021年的三個CVPR研討會最佳論文獎。他對學術界與產業界的交匯點以及機器學習與高度自動駕駛的結合充滿興趣。
哈諾·戈特沙克（Hanno Gottschalk）學習物理和數學，並於1995年和1997年分別獲得德國魯爾大學（Ruhr University Bochum）的學位。1999年完成數學物理的博士學位後，他在羅馬的拉薩比恩大學（University La Sapienza）進行了一年的博士後研究，隨後在波恩大學繼續其學術生涯，並於2003年在數學領域獲得資格認證。自2005年以來，他在波恩大學擔任講師（C2），並於2007年至2011年期間加入西門子能源，擔任概率設計的核心能力擁有者。自2011年以來，他在伍珀塔爾大學（University of Wuppertal）擔任隨機過程教授。2018年，他成為伍珀塔爾大學跨學科機器學習與數據分析中心（IZMD）的共同創始主任。他在深度學習領域的研究專注於深度學習感知算法的不確定性和安全性。應用領域包括假陽性和假陰性預測，以及分佈外物體的檢測和檢索。除了與大眾汽車（Volkswagen）和Aptiv的雙邊合作外，他還是AI驗證、AI Delta學習和AI數據工具等大型項目中的AI家族聯盟的成員。哈諾·戈特沙克將其作為統計學家和數學家的專業知識帶入自動駕駛領域，並將其與深度學習的尖端技術相結合。
塞巴斯蒂安·霍本（Sebastian Houben）在哈根大學學習數學和計算機科學，並於2009年畢業。他在魯爾大學（Ruhr University of Bochum）攻讀博士學位，並於2015年以優異成績畢業。完成波恩大學的博士後研究後，他被任命為魯爾大學的應用計算機科學助理教授，負責實時計算機視覺小組。自2020年初以來，他在弗勞恩霍夫智能分析與信息系統研究所擔任高級研究員。他的研究興趣涵蓋自動化機器人的計算機視覺和環境感知，特別是在自動駕駛領域。在KI-Absicherung聯盟和機器學習-萊茵-魯爾（Machine-Learning-Rhein-Ruhr, ML2R）能力中心中，他代表可信AI主題，特別關注黑箱模型的可解釋性實用方法、神經網絡中的不確定性估計和視覺分析。塞巴斯蒂安·霍本認為，如果人工智慧能夠更簡單、更穩健和更安全地使用，將會成為更強大的技術。他在弗勞恩霍夫的角色使他能夠促進從研究實驗室到實際應用的轉移。
