  Sensor visibility is crucial for safety-critical applications in automotive,
robotics, smart infrastructure and others: In addition to object detection and
occupancy mapping, visibility describes where a sensor can potentially measure
or is blind. This knowledge can enhance functional safety and perception
algorithms or optimize sensor topologies.
  Despite its significance, to the best of our knowledge, neither a common
definition of visibility nor performance metrics exist yet. We close this gap
and provide a definition of visibility, derived from a use case review. We
introduce metrics and a framework to assess the performance of visibility
estimators.
  Our metrics are verified with labeled real-world and simulation data from
infrastructure radars and cameras: The framework easily identifies false
visible or false invisible estimations which are safety-critical.
  Applying our metrics, we enhance the radar and camera visibility estimators
by modeling the 3D elevation of sensor and objects. This refinement outperforms
the conventional planar 2D approach in trustfulness and thus safety.
