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.

本文针对 GoA 4 低速货运列车的障碍物检测功能设计，提出了一种定量风险评估方法，使用统计方法和参数随机模型检查的组合对概率进行评估，最终构建了以三个独立的双通道模块和一个选民为核心的 3oo3 模型，可以对误分类的剩余风险进行评估，验证实现了使用机器学习的安全性。