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

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Safety Assurance of Machine Learning for Perception Functions to be defined and argued. At the same time, the use of machine learning (ML) functions is increasingly seen as a prerequisite to achieving the necessary levels of perception performance in the complex operating environments of these functions. This inevitably leads to the question of which supporti

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A Variational Deep Synthesis Approach for Perception Validationctionality of these systems, specifically in the context of automated driving. The main contributions are the introduction of a generative, parametric description of three-dimensional scenarios in a validation parameter space, and layered scene generation process to reduce the computational effort.

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The Good and the Bad: Using Neuron Coverage as a DNN Validation Techniquecode 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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大酒杯

Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetyencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent

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Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?f training data, architecture, and training are kept separate or independence is trained using special loss functions. Using data from different sensors (realized by up to five 2D projections of the 3D MNIST dataset) in our experiments is more efficiently reducing correlations, however not to an ext

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Cpr951

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