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.

翻译：本文探讨了一种面向自动化等级（GoA）4级低速货运列车障碍物检测功能设计的定量风险评估方法。该方法包含五个步骤，从单一检测通道开始，最终构建由三个独立双通道模块与一个表决器组成的三取二（3oo3）模型。通过统计方法与参数化随机模型检验相结合的方式，本文示例性地进行了概率评估。研究表明，在若干并非不合理的假设条件下，所得风险率对于特定应用场景是可接受的。针对卷积神经网络与传统图像处理软件中误分类残余风险的统计评估表明，即便其实施过程涉及现实中的机器学习不确定性，该安全关键性障碍物检测功能仍可具备高可信度。