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Deep Neural Networks and Data for Automated Driving: Robustness, Uncertainty Quantification, and Insights Towards Safety
Paperback
Technology & EngineeringGeneral MathematicsProgramming
ISBN13: 9783031012358
Publisher: Springer Nature
Published: Jul 22 2022
Pages: 448
Weight: 1.37
Height: 0.91 Width: 6.14 Depth: 9.21
Language: English
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?
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