Safety Assurance of Machine Learning for Perception Functions
The latest generation of safety standards applicable to automated driving systems require both qualitative and quantitative safety acceptance criteria to be defined and argued. At the same time, the use of machine learning (ML) functions is increasingly seen as a prerequisite to achieving the necessary levels of perception performance in the complex operating environments of these functions. This inevitably leads to the question of which supporting evidence must be presented to demonstrate the safety of ML-based automated driving systems. This chapter discusses the challenge of deriving suitable acceptance criteria for the ML function and describes how such evidence can be structured in order to support a convincing safety assurance case for the system. In particular, we show how a combination of methods can be used to estimate the overall machine learning performance, as well as to evaluate and reduce the impact of ML-specific insufficiencies, both during design and operation.