In the virtual validation of automated driving, trustworthy simulation models of perception sensors are required. Radar sensors are particularly hard to model, as their measurements are notoriously difficult to interpret. This is due to their complex measurement principle, involving multipath propagation of mm-waves, varying backscattering characteristics of objects, and further factors such as limited measurement ranges and resolutions that introduce uncertainty to the measurements. This work presents a method for studying the backscatter characteristics of vehicles under real-world driving conditions. A slalom-like driving scenario, which is representative of road driving where the vehicle is visible under different aspect angles, has been designed. It aims at a high level of reproducibility of the trajectories driven by the vehicles, hence reducing additional sources of uncertainty that were otherwise present in the measurements. In a large-scale measurement campaign, 13 vehicles have been studied. The vehicles under test are observed by multiple radars, mounted at different heights, and carry reference sensors for obtaining their positions. In this article, we present the measurement campaign and show major findings from our measurement results. Our focus lies on drawing conclusions for trustworthy sensor simulation. Both sensor measurement data and MATLAB code for data analysis are made publicly available alongside this article.

中文翻译：

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非均匀稀疏傅里叶变换及其应用


在自动驾驶的虚拟验证中，需要可信的感知传感器仿真模型。雷达传感器特别难以建模，因为它们的测量结果很难解释。这是由于它们的测量原理复杂，涉及毫米波的多径传播、物体不同的反向散射特性，以及有限的测量范围和分辨率等其他因素，这些因素会给测量带来不确定性。这项工作提出了一种研究真实驾驶条件下车辆后向散射特性的方法。设计了一种类似回转的驾驶场景，它代表了道路驾驶，其中车辆在不同的方位角下都是可见的。它的目标是车辆驱动的轨迹具有高水平的可重复性，从而减少测量中存在的额外不确定性来源。在大规模测量活动中，研究了 13 辆车辆。被测车辆通过安装在不同高度的多个雷达进行观察，并携带参考传感器来获取其位置。在本文中，我们介绍了测量活动并展示了测量结果的主要发现。我们的重点在于得出值得信赖的传感器模拟的结论。传感器测量数据和用于数据分析的 MATLAB 代码均与本文一起公开提供。