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The market introduction of automated vehicles has motivated intense researchefforts into the safety of automated vehicle systems. Unlike driver assistancesystems, SAE Level 3+ systems are not only responsible for executing (parts of)the dynamic driving task (DDT), but also for monitoring the automation system'sperformance at all times. Key components to fulfill these surveillance tasksare system monitors which can assess the system's performance at runtime, e.g.to activate fallback modules in case of partial system failures. In order toimplement reasonable monitoring strategies for an automated vehicle, holisticsystem-level approaches are required, which make use of sophisticated internalsystem models. In this paper we present definitions and an according taxonomy,subsuming such models as a vehicle's self-representation and highlight theterms' roles in a scene and situation representation. Holistic system-levelmonitoring does not only provide the possibility to use monitors for theactivation of fallbacks. In this paper we argue, why holistic system-levelmonitoring is a crucial step towards higher levels of automation, and give anexample how it also enables the system to react to performance loss at atactical level by providing input for decision making.

Supporting Safe Decision Making Through Holistic System-Level  Representations & Monitoring -- A Summary and Taxonomy of Self-Representation  Concepts for Automated Vehicles