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.

KI-LOK 研究项目旨在开发认证控制架构，用于防止基于人工智能感知系统中交通标志的误报检测，并通过整合 Python 原型系统和目标检测模型来测量相关性能指标。初始结果表现有希望，精度提高明显，但还需进一步研究泛化可能性。