@inproceedings{Cheng2018a, author = {Cheng, Chih-Hong and Diehl, Frederik and Hinz, Gereon Michael and Hamza, Yassine and N{\"{u}}hrenberg, Georg and Rickert, Markus and Rue{\ss}, Harald and Truong Le, Michael}, title = {Neural Networks for Safety-Critical Applications - {C}hallenges, Experiments and Perspectives}, booktitle = {Proceedings of the Design, Automation \\& Test in Europe Conference \\& Exhibition (DATE)}, pages = {1005--1006}, year = {2018}, month = mar, address = {Dresden, Germany}, 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.}, doi = {10.23919/DATE.2018.8342158}, keywords = {autonomous driving, robotics, neural networks, safety}, }