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</script>Future safety-critical systems will be highly automated or even autonomous and they will dynamically cooperate with other systems as part of a comprehensive ecosystem. This together with increasing utilization of artificial intelligence introduces uncertainties on different levels, which detriment the application of established safety engineering methods and standards. These uncertainties might be tackled by making systems safety-aware and enabling them to manage themselves accordingly. This paper introduces a corresponding conceptual dynamic safety management framework incorporating monitoring facilities and runtime safety-models to create safety-awareness. Based on this, planning and execution of safe system optimizations can be carried out by means of self-adaptation. We illustrate our approach by applying it for the dynamic safety assurance of a single car.
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 22 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
