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

Chih-Hong Cheng; Frederik Diehl; Georg N\"uhrenberg; Gereon Hinz; Harald Ruess; Markus Rickert; Michael Troung-Le; Yassine Hamza

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

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arxiv 1709.00911 v1 pith:SIFLNCMB submitted 2017-09-04 cs.SE cs.LG

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

Chih-Hong Cheng , Frederik Diehl , Yassine Hamza , Gereon Hinz , Georg N\"uhrenberg , Markus Rickert , Harald Ruess , Michael Troung-Le This is my paper

classification cs.SE cs.LG

keywords designingnetworksneuralrightvehicleann-basedanotherapplications

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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.

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

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