Approach to Define the Reliability of Safety-Related Machine Learning Based Functions in Highly Automated Driving

Abstract

Current standards cannot cover the safety requirements of machine learning based functions used in highly automated driving. Because of the opacity of neural networks, some self-driving functions cannot be developed following the V-model. These functions require the expansion of the standards. This paper focuses on this gap and defines functional reliability for such functions to help the future standards control the quality of machine learning based functions. As an example, reliability functions for pedestrian detection are built. Since the quality criteria in computer vision do not consider safety, new approaches for expression and evaluation of this reliability are designed.