Timing Model for Predictive Simulation of Safety-Critical Systems

Emerging evidence shows that safety-critical systems are evolving towards operating in uncertain context
while integrating intelligent software that evolves over time as well. Such behavior is considered to be unknown
at every moment in time because when faced with a similar situation, these systems are expected to
display an improved behavior based on artificial learning. Yet, a correct learning and knowledge-building process
for the non-deterministic nature of an intelligent evolution is still not guaranteed and consequently safety
of these systems cannot be assured. In this context, the approach of predictive simulation enables runtime
predictive evaluation of a system behavior and provision of quantified evidence of trust that enables a system
to react safety in case malicious deviations, in a timely manner.
For enabling the evaluation of timing behavior in a predictive simulation setting, in this paper we introduce
a general timing model that enables the virtual execution of a system’s timing behavior. The predictive evaluation
of the timing behavior can be used to evaluate a system’s synchronization capabilities and in case of
delays, trigger a safe fail-over behavior. We iterate our concept over an use case from the automotive domain
by considering two safety critical situations.