  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.
