Due to their ability to efficiently process unstructured and highly dimensional input data, machine learning algorithms are being applied to perception tasks for highly automated driving functions. The consequences of failures and insu_ciencies in such algorithms are severe and a convincing assurance case that the algorithms meet certain safety requirements is therefore required. However, the task of demonstrating the performance of such algorithms is non-trivial, and as yet, no consensus has formed regarding an appropriate set of verification measures. This paper provides a framework for reasoning about the contribution of performance evidence to the assurance case for machine learning in an automated driving context and applies the evaluation criteria to a pedestrian recognition case study.
|Title of host publication||Safecomp 2019 - Workshop on Artificial Intelligence Safety Engineering (Waise) of the 38th International Conference on Computer Safety, Reliability and Security|
|Publication status||Accepted/In press - 6 Jun 2019|