Lydia Gauerhof

Research Engineer Bosch

Agenda Day 1

Tuesday, October 22nd, 2019

9:10 AM Increasing confidence in evidences of performance in machine learning for highly automated driving functions

The safety of Deep Learning in perception of automated driving is challenging to proof due to its
complex environment, incomplete requirements and the uncertainty in the processing of data
including deep neural network. The consequences of failures and insufficiencies 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.

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