Timing Model for Predictive Simulation of Safety-Critical Systems
| Authors | |
|---|---|
| Year of publication | 2022 |
| Type | Article in Proceedings |
| Conference | 17th International Conference on Software Technologies (ICSOFT 2022) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.5220/0011317000003266 |
| Keywords | Runtime Prediction; Predictive Simulation; Malicious Behavior; Virtual Evaluation; Trust; Automotive |
| Description | 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. |
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