Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight Trains

Probabilistic Risk Assessment of an Obstacle Detection System for GoA 4 Freight Trains

연구 분야: Verification

학회: FTSCS 2023: Proceedings of the 9th ACM SIGPLAN International Workshop on Formal Techniques for Safety-Critical Systems

We propose a quantitative risk assessment approach for the design of an obstacle detection function for low-speed freight trains with grade of automation 4. In this five-step approach, starting with single detection channels and ending with a three-out-of-three model comprised of three independent dual-channel modules and a voter, we exemplify a probabilistic assessment, using a combination of statistical methods and parametric stochastic model checking. We illustrate that, under certain not unreasonable assumptions, the resulting hazard rate becomes acceptable for the discussed application setting. The statistical approach for assessing the residual risk of misclassifications in convolutional neural networks and conventional image processing software suggests that high confidence can be placed into the safety-critical obstacle detection function, even though its implementation involves realistic machine learning uncertainties.