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Deep Neural Networks and Data for Automated Driving Robustness, Uncertainty Quantification, and Insights Towards Safety /

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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 testi...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Fingscheidt, Tim (Editor), Gottschalk, Hanno (Editor), Houben, Sebastian (Editor)
Format: Electronic eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2022.
Edition:1st ed. 2022.
Subjects:
Automotive engineering.
Neural networks (Computer science) .
Computer vision.
Engineering > Data processing.
Automotive Engineering.
Mathematical Models of Cognitive Processes and Neural Networks.
Computer Vision.
Data Engineering.
Online Access:Link to Metadata
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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 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.

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