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.

本文提出了感知误差模型（PEM），并针对 Apollo 和 nuScences 两个开源驾驶软件进行了参数建模和评估。通过在 SVL 等开源车辆模拟器中实现 PEM，我们通过评估相机、光达和相机 - 光达设置，展示了基于 PEM 的虚拟测试的实用性，并发现其中的局限性。

自主驾驶车辆中感知误差的模拟

On the Simulation of Perception Errors in Autonomous Vehicles

In this paper, we introduce the notion of Cooperative Perception Error Models
(coPEMs) towards achieving an effective and efficient integration of V2X
solutions within a virtual test environment. We focus our analysis on the
occlusion problem in the (onboard) perception of Autonomous Vehicles (AV),
which can manifest as misdetection errors on the occluded objects. Cooperative
perception (CP) solutions based on Vehicle-to-Everything (V2X) communications
aim to avoid such issues by cooperatively leveraging additional points of view
for the world around the AV. This approach usually requires many sensors,
mainly cameras and LiDARs, to be deployed simultaneously in the environment
either as part of the road infrastructure or on other traffic vehicles.
However, implementing a large number of sensor models in a virtual simulation
pipeline is often prohibitively computationally expensive. Therefore, in this
paper, we rely on extending Perception Error Models (PEMs) to efficiently
implement such cooperative perception solutions along with the errors and
uncertainties associated with them. We demonstrate the approach by comparing
the safety achievable by an AV challenged with a traffic scenario where
occlusion is the primary cause of a potential collision.

本文介绍了合作感知误差模型（coPEMs）的概念，旨在实现 V2X 解决方案在虚拟测试环境中的有效和高效集成。我们聚焦于自动驾驶车辆（AV）的感知中的遮挡问题，并通过扩展感知误差模型（PEMs）来有效实施这种合作感知解决方案及其相关的错误和不确定性。