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:尽管人们公认自动飞行器的虚拟测试对于安全评估至关重要,但AV模拟器仍在积极发展之中,一个特别具有挑战性的问题是将遥感和感知子系统有效纳入模拟循环。在本条中,我们定义了感知错误模型(PEM),这是一个虚拟模拟组件,可以分析感知错误对AV安全的影响,而无需对传感器本身进行模拟。我们提议采用一种普遍的数据驱动程序,对参数进行模拟,并使用开放源驱动软件阿波罗和公开AV数据集(nuScenses)对模型进行评估。此外,我们在SVL(一个开源汽车模拟器)实施PEMs。此外,我们通过评价相机、LIDAR和摄像-LiDAR设置,展示了基于PEM的虚拟测试的效用。我们的虚拟测试突出了当前评价指标中的局限性,拟议的方法可以帮助研究感知错误对AV安全的影响。