用仿真方法量化汽车感知系统的性能

Zhenyi Liu, Devesh Shah, Alireza Rahimpour, Devesh Upadhyay, Joyce Farrell, Brian Wandell
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摘要

复杂系统的设计和评估可以受益于软件模拟-有时被称为数字孪生。模拟可用于表征系统性能或测试其在难以测量的条件下的性能(例如,汽车感知系统的夜间)。我们描述了我们用来评估物体(汽车)检测图像系统性能的图像系统仿真软件工具。我们描述了用13种不同的光学和像素大小的相机进行的实验。为了测量相机空间分辨率的影响,我们设计了一组驾驶场景,其中有许多不同距离的汽车。我们通过测量平均精度来量化系统性能,并报告了与系统分辨率和目标检测性能相关的趋势。我们还量化了所有摄像机和COCO预训练网络在夜间条件下与白天相比的较大性能下降。
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Using simulation to quantify the performance of automotive perception systems
The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance or to test its performance under conditions that are difficult to measure (e.g., nighttime for automotive perception systems). We describe the image system simulation software tools that we use to evaluate the performance of image systems for object (automobile) detection. We describe experiments with 13 different cameras with a variety of optics and pixel sizes. To measure the impact of camera spatial resolution, we designed a collection of driving scenes that had cars at many different distances. We quantified system performance by measuring average precision and we report a trend relating system resolution and object detection performance. We also quantified the large performance degradation under nighttime conditions, compared to daytime, for all cameras and a COCO pre-trained network.
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