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自动驾驶汽车安全评估的关键手段，但自动驾驶模拟器仍处于持续开发阶段。其中一项极具挑战性的问题是如何将感知与认知子系统有效集成至仿真循环中。本文定义了感知误差模型——一种无需对传感器本身建模即可分析感知误差对自动驾驶安全影响的虚拟仿真组件。我们提出了一种基于数据驱动的通用参数化建模方法，并利用开源驾驶软件Apollo及公开自动驾驶数据集nuScenes进行验证评估。此外，我们还在开源车辆模拟器SVL中实现了PEM。通过评估纯相机、纯激光雷达及相机-激光雷达融合配置，进一步验证了基于PEM的虚拟测试的有效性。我们的虚拟测试揭示了当前评估指标的局限性，而所提方法有助于研究感知误差对自动驾驶安全的影响。