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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.
Part I Safe AI—An Overview
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Part II Recent Advances in Safe AI for Automated Driving
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?
Analysis and Comparison of Datasets by Leveraging Data Distributions in Latent Spaces
Optimized Data Synthesis for DNN Training and Validation by Sensor Artifact Simulation
Improved DNN Robustness by Multi-task Training with an Auxiliary Self-Supervised Task
Improving Transferability of Generated Universal Adversarial Perturbations for Image Classification and Segmentation
Invertible Neural Networks for Understanding Semantics of Invariances of CNN Representations
Robin Rombach, Patrick Esser, Andreas Blattmann, and Björn Ommer Confidence Calibration for Object Detection and Segmentation
Uncertainty Quantification for Object Detection: Output and Gradient-Based Approaches
Detecting and Learning the Unknown in Semantic Segmentation
Evaluating Mixture-of-Experts Architectures for Network Aggregation
Safety Assurance of Machine Learning for Perception Functions
A Variational Deep Synthesis Approach for Perception Validation
The Good and the Bad: Using Neuron Coverage as a DNN Validation Technique
Joint Optimization for DNN Model Compression and Corruption Robustness