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.

翻译：尽管虚拟测试已被公认为评估自动驾驶车辆（AV）安全性的必要手段，但AV模拟器仍处于积极开发阶段。其中一个尤为具有挑战性的问题是如何将感知子系统有效纳入模拟循环中。本文定义了一种虚拟仿真组件——感知误差模型（PEM），该组件无需对传感器本身建模即可分析感知误差对AV安全性的影响。我们提出了一种通用的数据驱动参数化建模流程，并利用开源驾驶软件Apollo和公开AV数据集nuScenes对其进行评估。此外，我们在开源车辆模拟器SVL中实现了PEM。最后，通过评估纯摄像头、纯LiDAR以及摄像头-LiDAR混合配置方案，我们展示了基于PEM的虚拟测试的有效性。虚拟测试揭示了当前评估指标的局限性，而所提出的方法有助于研究感知误差对AV安全性的影响。