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Dokumenttyp:
Konferenzbeitrag 
Autor(en):
Cheng, Chih-Hong; Diehl, Frederik; Hinz, Gereon; Hamza, Yassine; Nührenberg, Georg; Rickert, Markus; Rueß, Harald; Truong-Le, Michael 
Titel:
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives 
Abstract:
We propose a methodology for designing dependable Artificial Neural Networks (ANNs) 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 for designing a highway 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. 
Kongress- / Buchtitel:
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE) 
Konferenzort:
Dresden, Germany 
Jahr:
2018 
Monat:
Mar 
Seiten:
1005--1006