  The safety of automated vehicles (AVs) relies on the representation of their
environment. Consequently, state-of-the-art AVs employ potent sensor systems to
achieve the best possible environment representation at all times. Although
these high-performing systems achieve impressive results, they induce
significant requirements for the processing capabilities of an AV's
computational hardware components and their energy consumption.
  To enable a dynamic adaptation of such perception systems based on the
situational perception requirements, we introduce a model-agnostic method for
the scalable employment of single-frame object detection models using
frame-dropping in tracking-by-detection systems. We evaluate our approach on
the KITTI 3D Tracking Benchmark, showing that significant energy savings can be
achieved at acceptable performance degradation, reaching up to 28% reduction of
energy consumption at a performance decline of 6.6% in HOTA score.
