  The market introduction of automated vehicles has motivated intense research
efforts into the safety of automated vehicle systems. Unlike driver assistance
systems, SAE Level 3+ systems are not only responsible for executing (parts of)
the dynamic driving task (DDT), but also for monitoring the automation system's
performance at all times. Key components to fulfill these surveillance tasks
are 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 to
implement reasonable monitoring strategies for an automated vehicle, holistic
system-level approaches are required, which make use of sophisticated internal
system models. In this paper we present definitions and an according taxonomy,
subsuming such models as a vehicle's self-representation and highlight the
terms' roles in a scene and situation representation. Holistic system-level
monitoring does not only provide the possibility to use monitors for the
activation of fallbacks. In this paper we argue, why holistic system-level
monitoring is a crucial step towards higher levels of automation, and give an
example how it also enables the system to react to performance loss at a
tactical level by providing input for decision making.
