There is considerable industrial interest in integrating AI techniques into railway systems, notably for fully autonomous train systems. The KI-LOK research project is involved in developing new methods for certifying such AI-based systems. Here we explore the utility of a certified control architecture for a runtime monitor that prevents false positive detection of traffic signs in an AI-based perception system. The monitor uses classical computer vision algorithms to check if the signs -- detected by an AI object detection model -- fit predefined specifications. We provide such specifications for some critical signs and integrate a Python prototype of the monitor with a popular object detection model to measure relevant performance metrics on generated data. Our initial results are promising, achieving considerable precision gains with only minor recall reduction; however, further investigation into generalization possibilities will be necessary.

翻译：工业界对将人工智能技术集成到铁路系统中有着浓厚兴趣，尤其是用于全自动列车系统。KI-LOK研究项目致力于开发新型方法来认证此类基于AI的系统。本文探讨了一种认证控制架构在运行时监控器中的效用，该监控器可防止基于AI的感知系统中对交通标志的误检。该监控器使用经典计算机视觉算法来检查由AI目标检测模型检测到的标志是否符合预定义的规范。我们为部分关键标志提供了此类规范，并将该监控器的Python原型与主流目标检测模型集成，以测量生成数据上的相关性能指标。初步结果令人鼓舞，在仅轻微降低召回率的情况下实现了显著的精度提升；然而，仍需进一步研究其泛化可能性。