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

Chih-Hong Cheng, Frederik Diehl, Gereon Michael Hinz, Yassine Hamza, Georg Nührenberg, Markus Rickert, Harald Rueß und Michael Truong Le

Proceedings of the Design, Automation & Test in Europe Conference & Exhibition (DATE), pp. 1005–1006

März 2018 · Dresden, Germany · DOI: 10.23919/DATE.2018.8342158

Zusammenfassung

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

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