  Within the field of automated driving, a clear trend in environment
perception tends towards more sensors, higher redundancy, and overall increase
in computational power. This is mainly driven by the paradigm to perceive the
entire environment as best as possible at all times. However, due to the
ongoing rise in functional complexity, compromises have to be considered to
ensure real-time capabilities of the perception system.
  In this work, we introduce a concept for situation-aware environment
perception to control the resource allocation towards processing relevant areas
within the data as well as towards employing only a subset of functional
modules for environment perception, if sufficient for the current driving task.
Specifically, we propose to evaluate the context of an automated vehicle to
derive a multi-layer attention map (MLAM) that defines relevant areas. Using
this MLAM, the optimum of active functional modules is dynamically configured
and intra-module processing of only relevant data is enforced.
  We outline the feasibility of application of our concept using real-world
data in a straight-forward implementation for our system at hand. While
retaining overall functionality, we achieve a reduction of accumulated
processing time of 59%.
