Deep Neural Networks and Data for Automated Driving
Deep Neural Networks and Data for Automated Driving:
Robustness, Uncertainty Quantification, and Insights Towards Safety
English | 2022 | ISBN: 3031012321 | 445 Pages | PDF EPUB | 74 MB
Robustness, Uncertainty Quantification, and Insights Towards Safety
English | 2022 | ISBN: 3031012321 | 445 Pages | PDF EPUB | 74 MB
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?