  In this paper, a quantitative risk assessment approach is discussed for the
design of an obstacle detection function for low-speed freight trains with
grade of automation (GoA)~4. In this 5-step approach, starting with single
detection channels and ending with a three-out-of-three (3oo3) model
constructed of three independent dual-channel modules and a voter, a
probabilistic assessment is exemplified, using a combination of statistical
methods and parametric stochastic model checking. It is illustrated that, under
certain not unreasonable assumptions, the resulting hazard rate becomes
acceptable for specific application settings. The statistical approach for
assessing the residual risk of misclassifications in convolutional neural
networks and conventional image processing software suggests that high
confidence can be placed into the safety-critical obstacle detection function,
even though its implementation involves realistic machine learning
uncertainties.
