Titlebook: Deep Neural Networks and Data for Automated Driving; Robustness, Uncertai Tim Fingscheidt,Hanno Gottschalk,Sebastian Houben Book‘‘‘‘‘‘‘‘ 20

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§ 55 Wertpapiererwerbs- und übernahmegesetzcode coverage in software testing, has been proposed as one such V&V method. We provide a summary of different neuron coverage variants and their inspiration from traditional software engineering V&V methods. Our first experiment shows that novelty and granularity are important considerations when a

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Deutsches und internationales Steuerrechtare trained on. In this chapter, we address two insufficiencies of DNNs, namely, the lack of robustness to corruptions in the data, and the lack of real-time deployment capabilities, that need to be addressed to enable their safe and efficient deployment in real-time environments. We introduce hybri

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rent knowledge in neural networks and AI.Provides a basis fo.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 man