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Computer Science > Computer Vision and Pattern Recognition

arXiv:2303.00983 (cs)
[Submitted on 2 Mar 2023 (v1), last revised 10 Mar 2023 (this version, v2)]

Title:Using simulation to quantify the performance of automotive perception systems

Authors:Zhenyi Liu, Devesh Shah, Alireza Rahimpour, Devesh Upadhyay, Joyce Farrell, Brian A Wandell
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Image and Video Processing (eess.IV)
Cite as: arXiv:2303.00983 [cs.CV]
  (or arXiv:2303.00983v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2303.00983

Submission history

From: Brian Wandell [view email]
[v1] Thu, 2 Mar 2023 05:28:35 UTC (17,284 KB)
[v2] Fri, 10 Mar 2023 21:15:28 UTC (17,284 KB)
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