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In this paper, a quantitative risk assessment approach is discussed for thedesign of an obstacle detection function for low-speed freight trains withgrade of automation (GoA)~4. In this 5-step approach, starting with singledetection channels and ending with a three-out-of-three (3oo3) modelconstructed of three independent dual-channel modules and a voter, aprobabilistic assessment is exemplified, using a combination of statisticalmethods and parametric stochastic model checking. It is illustrated that, undercertain not unreasonable assumptions, the resulting hazard rate becomesacceptable for specific application settings. The statistical approach forassessing the residual risk of misclassifications in convolutional neuralnetworks and conventional image processing software suggests that highconfidence can be placed into the safety-critical obstacle detection function,even though its implementation involves realistic machine learninguncertainties.

Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight Trains