Conformal prediction for trustworthy detection of railway signals

We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals. State-of-the-art architectures are tested and the most promising one undergoes the process of conformalization, where a correction is applied to the predicted...

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Bibliographic Details
Published inAi and ethics (Online) Vol. 4; no. 1; pp. 157 - 161
Main Authors Andéol, Léo, Fel, Thomas, de Grancey, Florence, Mossina, Luca
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.02.2024
Subjects
Artificial Intelligence
Computer Science
Ethics
Original Research
Railway signaling
Safety critical
Object detection
Conformal prediction
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Summary:We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals. State-of-the-art architectures are tested and the most promising one undergoes the process of conformalization, where a correction is applied to the predicted bounding boxes (i.e., to their height and width) such that they comply with a predefined probability of success. We work with a novel exploratory dataset of images taken from the perspective of a train operator, as a first step to build and validate future trustworthy machine learning models for the detection of railway signals.
ISSN:2730-5953
2730-5961
DOI:10.1007/s43681-023-00400-7