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arXiv · 1709.00911

Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives

Chih-Hong Cheng·Frederik Diehl·Yassine Hamza·Gereon Hinz·Georg Nührenberg·Markus Rickert·Harald Ruess·Michael Troung-Le
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Abstract

We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards. We apply the concept in a concrete case study in designing a high-way ANN-based motion predictor to guarantee safety properties such as impossibility for the ego vehicle to suggest moving to the right lane if there exists another vehicle on its right.

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Chih-Hong Cheng, Frederik Diehl, Yassine Hamza, Gereon Hinz, Georg Nührenberg, Markus Rickert, Harald Ruess, Michael Troung-Le. 2017-09-04. Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives. https://arxiv.org/abs/1709.00911

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