Safer Than Perception: Assuring Confidence in Safety-Critical Decisions of Automated Vehicles.

We address one of the key challenges in assuring safety of autonomous cyber-physical systems that rely on learning-enabled classification within their environmental perception: How can we achieve confidence in the perception chain, especially when dealing with percepts safe-guarding critical manoeuvres? We present a methodology which allows to mathematically prove that the risk of misevaluating a safety-critical guard conditions referring to environmental artefacts can be bounded to a considerably lower frequency than the risk of individual misclassifications, and can thereby be adjusted to a value less than a given level of societally accepted risk.