Vehicle safety depends on (a) the range of identified hazards and (b) the operational situations for which mitigations of these hazards are acceptably decreasing risk. Moreover, with an increasing degree of autonomy, risk ownership is likely to increase for vendors towards regulatory certification. Hence, highly automated vehicles have to be equipped with verified controllers capable of reliably identifying and mitigating hazards in all possible operational situations. To this end, available methods for the design and verification of automated vehicle controllers have to be supported by models for hazard analysis and mitigation.
G "udemann and Ortmeier @cite_7 present a language for probabilistic system modeling for safety analysis. Formalized as , they propose two ways of failure mode modeling ( per-time and per-demand failure modes), and two ways of deductive cause consequence reasoning ( quantitative and qualitative). Their model and reasoning can extend our approach. However, our work (i) adds stronger guidelines on how to build planning models and (ii) puts hazard analysis into the context of autonomous systems and mitigation planning.
@cite_7
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In model-based safety analysis both qualitative aspects i.e. what must go wrong for a system failure) and quantitative aspects (i.e. how probable is a system failure) are very important. For both aspects methods and tools are available. However, until now for each aspect new and independent models must be built for analysis. This paper proposes the SAML framework as a formal foundation for both qualitative and quantitative formal model-based safety analysis. The main advantage of SAML is the combination of qualitative and quantitative formal semantics which allows different analyses on the same model. This increases the confidence in the analysis results, simplifies modeling and is less error-prone. The SAML framework is tool-independent. As proof-of-concept, we present sound transformation of the formalism into two state of the art model-checking notations. Prototypical tool support for the sound transformation of SAML into PRISM and MRMC for probabilistic analysis as well as different variants of the SMV model checker for qualitative analysis is currently being developed.