Formal Development of Safe Automated Driving Using Differential Dynamic Logic

Journal article, 2023

The challenges in providing convincing arguments for safe and correct behavior of automated driving (AD) systems have so far hindered their widespread commercial deployment. Conventional development approaches such as testing and simulation are limited by non-exhaustive analysis, and can thus not guarantee safety in all possible scenarios. Formal methods can provide mathematical proofs that could be used to produce rigorous evidence to support the safety argument. This paper investigates the use of differential dynamic logic and the deductive verification tool KeYmaera X in the development of an AD feature. Specifically, this paper demonstrates how formal models and safety proofs of different design variants of a Decision & Control module can be used in the safety argument of an in-lane AD feature. In doing so, the assumptions and invariant conditions necessary to guarantee safety are identified, and the paper shows how such an analysis helps during the development process in requirement refinement and formulation of the operational design domain. Furthermore, it is shown how the performance of the different models is formally analyzed exhaustively, in all their allowed behaviors.