  The development of Automated Vehicles (AVs) is progressing quickly and the
first robotaxi services are being deployed worldwide. However, to receive
authority certification for mass deployment, manufactures need to justify that
their AVs operate safer than human drivers. This in turn creates the need to
estimate and model the collision rate (failure rate) of an AV taking all
possible errors and driving situations into account. In other words, there is
the strong demand for comprehensive Mean Time Between Failure (MTBF) models for
AVs. In this paper, we will introduce such a generic and scalable model that
creates a link between errors in the perception system to vehicle-level
failures (collisions). Using this model, we are able to derive requirements for
the perception quality based on the desired vehicle-level MTBF or vice versa to
obtain an MTBF value given a certain mission profile and perception quality.
