Certified Control for Train Sign Classification
This addresses safety certification challenges for AI in autonomous railway systems, though it appears incremental with limited generalization.
The paper tackles the problem of false positive detection of traffic signs in AI-based railway perception systems by implementing a certified control architecture with runtime monitoring, achieving considerable precision gains with only minor recall reduction.
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.