  Advances in the field of environment perception for automated agents have
resulted in an ongoing increase in generated sensor data. The available
computational resources to process these data are bound to become insufficient
for real-time applications. Reducing the amount of data to be processed by
identifying the most relevant data based on the agents' situation, often
referred to as situation-awareness, has gained increasing research interest,
and the importance of complementary approaches is expected to increase further
in the near future. In this work, we extend the applicability range of our
recently introduced concept for situation-aware environment perception to the
decentralized automation architecture of the UNICARagil project. Considering
the specific driving capabilities of the vehicle and using real-world data on
target hardware in a post-processing manner, we provide an estimate for the
daily reduction in power consumption that accumulates to 36.2%. While achieving
these promising results, we additionally show the need to consider scalability
in data processing in the design of software modules as well as in the design
of functional systems if the benefits of situation-awareness shall be leveraged
optimally.
