Name
Abstract | ||
|---|---|---|
Artificial neural networks (NN) are instrumental in realizing highly-automated driving functionality. An overarching challenge is to identify best safety engineering practices for NN and other learning-enabled components. In particular, there is an urgent need for an adequate set of metrics for measuring all-important NN dependability attributes. We address this challenge by proposing a number of NN-specific and efficiently computable metrics for measuring NN dependability attributes including robustness, interpretability, completeness, and correctness.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MEMCOD.2018.8556962 | MEMOCODE |
Keywords | DocType | Volume |
dependability metrics,NN dependability attributes,highly-automated driving,learning-enabled components,artificial neural networks,efficiently computable metrics | Conference | abs/1806.02338 |
ISBN | Citations | PageRank |
978-1-5386-6196-3 | 5 | 0.39 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-Hong Cheng | 1 | 134 | 17.63 |
| Georg Nührenberg | 2 | 38 | 2.56 |
| Chung-Hao Huang | 3 | 85 | 7.55 |
| Harald Ruess | 4 | 95 | 10.86 |
| Hirotoshi Yasuoka | 5 | 34 | 2.38 |