OpenVX and Real-Time Certification: The Troublesome History
OpenVX and Real-Time Certification: The Troublesome History
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DOI：
10.1109/rtss46320.2019.00036
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Tanya Amert;S. Voronov;James H. Anderson
中科院分区：
文献类型：
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作者：
Tanya Amert;S. Voronov;James H. Anderson
自动驾驶汽车中使用的许多计算机视觉（CV）应用依赖于历史结果，这在处理图形时引入了周期。然而，现有的响应时间分析在存在周期的情况下会崩溃，要么完全失败，要么大幅牺牲并行性或CV准确性。为了解决这种情况下，本文提出了一种新的基于图的任务模型，基于最近批准的OpenVX标准，其中包括历史要求和他们的诱导周期作为一流的概念。使用这个模型，响应时间的界限，可能包含周期的图形。这些界限暴露了响应性和CV准确性之间的权衡，这取决于所允许的并行性的程度。通过涉及行人跟踪的CV案例研究说明了这种权衡。在本案例研究中，本文中提出的方法，使显着改善分析和观察的响应时间，可接受的CV精度相比，以前的方法。
Many computer-vision (CV) applications used in autonomous vehicles rely on historical results, which introduce cycles in processing graphs. However, existing response-time analysis breaks down in the presence of cycles, either by failing completely or by drastically sacrificing parallelism or CV accuracy. To address this situation, this paper presents a new graph-based task model, based on the recently ratified OpenVX standard, that includes historical requirements and their induced cycles as first-class concepts. Using this model, response-time bounds for graphs that may contain cycles are derived. These bounds expose a tradeoff between responsiveness and CV accuracy that hinges on the extent of allowed parallelism. This tradeoff is illustrated via a CV case study involving pedestrian tracking. In this case study, the methods proposed in this paper enabled significant improvements in both analytical and observed response times, with acceptable CV accuracy, compared to prior methods.