  Deploying deep learning models in real-world certified systems requires the
ability to provide confidence estimates that accurately reflect their
uncertainty. In this paper, we demonstrate the use of the conformal prediction
framework to construct reliable and trustworthy predictors for detecting
railway signals. Our approach is based on a novel dataset that includes images
taken from the perspective of a train operator and state-of-the-art object
detectors. We test several conformal approaches and introduce a new method
based on conformal risk control. Our findings demonstrate the potential of the
conformal prediction framework to evaluate model performance and provide
practical guidance for achieving formally guaranteed uncertainty bounds.
