Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety
暫譯: 自動駕駛的深度神經網絡與數據：穩健性、不確定性量化及安全性洞察

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.