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A safe automated vehicle must “know when it doesn’t know.” Automated vehicles cannot depend on the traditional drive-fail-fix cycle due to heavy tail problem distributions supplying virtually infinite problems. In order to be safe, automated vehicles require the ability to handle unforeseen untested “unknown unknown” situations. Safety Performance Indicators (SPIs) at deep-enough sub-claim levels can uncover safety case claim violations in a ‘leading’ fashion - prior to safety events. This paper introduces Dynamic Realtime SPIs (SPIs calculated at runtime) at sufficiently low safety case claim levels which yield runtime recognition of safety case claim violations and can be used by the ADS to infer that it is encountering an “unknown unknown” situation. Then, because “knowing when an ADS doesn’t know” is insufficient to ensure AV safety, we introduce the Dynamic Realtime SPI (DRSPI) framework, for handling such occurrences.

This paper focuses on stability of automotive supervisory control systems (ASCSs). It serves to introduce the concept of stability with respect to an entire ASCS. The realm of ASCSs is categorized and a brief description of pre-existing classical methods of stability analysis is presented. With the concept then having been fully introduced, an approach to evaluating stability of a key category of ASCS, the rule-based deterministic ASCS, is presented. This approach, cited from unrelated modern literature concerning stability of deterministic finite state machines, is novel in that its original target research area was not specifically automotive engineering.