Neural networks for safety-critical applications — Challenges, experiments and perspectives
Chih‐Hong Cheng, Frederik Diehl, Gereon Hinz, Yassine Hamza, Georg Nuehrenberg, Markus Rickert, Harald Rueß, Michael Truong Le · 2018
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.