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The safety of automated driving systems must be justified by convincingarguments and supported by compelling evidence to persuade certificationagencies, regulatory entities, and the general public to allow the systems onpublic roads. This persuasion is typically facilitated by compiling thearguments and the compelling evidence into a safety case. Reviews and testing,two common approaches to ensure correctness of automotive systems cannotexplore the typically infinite set of possible behaviours. In contrast, formalmethods are exhaustive methods that can provide mathematical proofs ofcorrectness of models, and they can be used to prove that formalizations offunctional safety requirements are fulfilled by formal models of systemcomponents. This paper shows how formal methods can provide evidence for thecorrect break-down of the functional safety requirements onto the componentsthat are part of feedback loops, and how this evidence fits into the argumentof the safety case. If a proof is obtained, the formal models are used asrequirements on the components. This structure of the safety argumentation canbe used to alleviate the need for reviews and tests to ensure that thebreak-down is correct, thereby saving effort both in data collection andverification time.

A Formal-Methods Approach to Provide Evidence in Automated-Driving Safety Cases