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.
@article{arxiv.1806.02338,
title = {Towards Dependability Metrics for Neural Networks},
author = {Chih-Hong Cheng and Georg Nührenberg and Chung-Hao Huang and Harald Ruess and Hirotoshi Yasuoka},
journal= {arXiv preprint arXiv:1806.02338},
year = {2018}
}