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

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)
Jahr:: 2018
Monat:: Mar
Seiten:: 1005--1006
Volltext / DOI:: doi:10.23919/DATE.2018.8342158
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