基于视频的环绕传感系统鲁棒性测试的降雨模拟

Dennis Hospach, Stefan Müller, W. Rosenstiel, O. Bringmann
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引用次数: 20

摘要

最近,光学传感器已经成为现代汽车的标配,在各种环境影响下进行必要的测试提出了问题。为了实现基于视觉的环绕传感系统的高测试覆盖率,需要对许多不同的环境条件进行测试。不幸的是,通过记录真实的视频数据来构建所有相关环境条件的测试集是非常耗时的。提出了一种环境感知虚拟样机和鲁棒性测试的新方法。我们提出了一种方法,可以显著减少用于设计和验证基于视觉的高级驾驶辅助系统(ADAS)和全自动驾驶所需的道路记录。我们的方法通过使用大量减少的真实道路记录,并在一个全面的虚拟原型环境中应用计算机生成的降雨变化,从而促进了可比测试集的生成。结合对相机属性的模拟,在很大程度上影响着降雨的视觉效果,我们可以在多种参数下生成不同的降雨场景。我们的方法已经应用于一个汽车车道检测系统,该系统使用了一系列多种降雨场景。我们已经探索了降雨如何影响这样的系统,以及如何使用模拟降雨场景来检测这种行为。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simulation of falling rain for robustness testing of video-based surround sensing systems
Recently, optical sensors have become a standard item in modern cars, raising questions with respect to the necessary testing under various ambient effects. In order to achieve a high test coverage of vision-based surround sensing systems, a lot of different environmental conditions need to be tested. Unfortunately, it is by far too time-consuming to build test sets of all relevant environmental conditions by recording real video data. This paper presents a novel approach for ambient-aware virtual prototyping and robustness testing. We propose a method to significantly reduce the needed on-road recordings being used for design and validation of vision-based Advanced Driver Assistance Systems (ADAS) and fully automated driving. Our approach facilitates the generation of comparable test sets by using largely reduced amounts of real on-road recordings and applying computer-generated variations of falling rain to it in a comprehensive virtual prototyping environment. In combination with the simulation of camera properties, which influence the visual effects of falling rain to a great extent, we are able to generate different rain scenarios under a wide variety of parameters. Our approach has been applied to an automotive lane detection system using a series of multiple rain scenarios. We have explored, how falling rain can influence such a system and how such behavior can be detected using simulated rain scenarios.
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