Name
Title | ||
|---|---|---|
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.23919/date.2018.8342158 | design, automation, and test in europe |
DocType | Volume | Citations |
Conference | abs/1709.00911 | 3 |
PageRank | References | Authors |
0.38 | 3 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-Hong Cheng | 1 | 134 | 17.63 |
| Frederik Diehl | 2 | 8 | 2.24 |
| Yassine Hamza | 3 | 6 | 0.76 |
| Yassine Hamza | 4 | 6 | 0.76 |
| Georg Nührenberg | 5 | 38 | 2.56 |
| Markus Rickert | 6 | 217 | 22.78 |
| Harald Ruess | 7 | 95 | 10.86 |
| Michael Troung-Le | 8 | 3 | 0.38 |