  Even though virtual testing of Autonomous Vehicles (AVs) has been well
recognized as essential for safety assessment, AV simulators are still
undergoing active development. One particularly challenging question is to
effectively include the Sensing and Perception (S&P) subsystem into the
simulation loop. In this article, we define Perception Error Models (PEM), a
virtual simulation component that can enable the analysis of the impact of
perception errors on AV safety, without the need to model the sensors
themselves. We propose a generalized data-driven procedure towards parametric
modeling and evaluate it using Apollo, an open-source driving software, and
nuScenes, a public AV dataset. Additionally, we implement PEMs in SVL, an
open-source vehicle simulator. Furthermore, we demonstrate the usefulness of
PEM-based virtual tests, by evaluating camera, LiDAR, and camera-LiDAR setups.
Our virtual tests highlight limitations in the current evaluation metrics, and
the proposed approach can help study the impact of perception errors on AV
safety.
