Analyzing Regions of Safety for Handling Shared Data in Cooperative Systems

Cooperative Systems promise increased performance by enriching environmental perception through shared data. Conversely, the entailed openness of the individual system architectures threatens their safety. Recent works focus on a runtime safety assessment to address this threat and thereby aim for high abstractions to provide general interfaces. On the other hand, uncertainty models of shared data, which are necessary inputs to such approaches, aim for low abstractions to provide detailed representations. The present work addresses the resulting incompatibilities by proposing a Lyapunov-based method to estimate so-called Regions of Safety. We show that these enable analyzing low-level uncertainty models to interface with state-of-the-art run-time safety assessment methods and thereby facilitate self-adaptivity and guaranteed safety of cooperative systems. The approach is evaluated in the simulated scenario of Cooperative Adaptive Cruise Control.