Deep Neural Networks and Data for Automated Driving : : Robustness, Uncertainty Quantification, and Insights Towards Safety / / edited by Tim Fingscheidt, Hanno Gottschalk, Sebastian Houben.

• TeilnehmendeR: Fingscheidt, Tim,; Gottschalk, Hanno,; Houben, Sebastian,
• Place / Publishing House: Cham : : Springer Nature,, 2022.
• Year of Publication: 2022
• Language: English
• Physical Description: 1 online resource (xviii, 427 pages) :; illustrations

"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."

Table of Contents

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 Classi{uFB01}cation and Segmentation Chapter 7. Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations Chapter 8. Con{uFB01}dence Calibration for Object Detection and Segmentation Chapter 9. Uncertainty Quanti{uFB01}cation 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. 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 Classi̐Ơѓcation and Segmentation Chapter 7. Invertible Neural Networks for Understanding Semantics of Invariances of CNN epresentations Chapter 8. Con̐Ơѓdence Calibration for Object Detection and Segmentation Chapter 9. Uncertainty Quanti̐Ơѓcation 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.