Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1005–1006
März 2018 · Dresden, Germany · DOI: 10.23919/DATE.2018.8342158
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.
Stichworte: autonomous driving, robotics, neural networks, safety