用学习方法检测汽车仪表盘警告标志是否正常显示，防误报且抗像素错误。
Learning-Based Error Detection System for Advanced Vehicle Instrument Cluster Rendering
- 通过学习区分人类能正确识别的警示标志与失效标志。
- 实验中所有异常模式均被准确识别，无误报。
- 适合车载显示系统质量监控，尤其适用复杂渲染效果场景。
汽车工业正不断拓展新型车型的数字显示屏功能，不仅在尺寸、分辨率和自定义选项上升级，还引入了叠加、变形等新显示效果。然而，这使得传统监测手段如循环冗余校验（CRC）失效，因为透明度混合、缩放或扭曲等操作会导致错误触发。为此，本文提出一种基于学习的新监测方法，以警示标志（如警告灯）为例，自动区分‘正常’标志（人类可正确识别）与‘损坏’标志（无法辨识）。该方法对单个像素错误具有天然鲁棒性，并隐式支持动态背景、叠加层及缩放效果。实验表明，所有异常测试样本均被正确分类，未产生任何误报。
原文摘要 · Abstract (English)
The automotive industry is currently expanding digital display options with every new model that comes onto the market. This entails not just an expansion in dimensions, resolution, and customization choices, but also the capability to employ novel display effects like overlays while assembling the content of the display cluster. Unfortunately, this raises the need for appropriate monitoring systems that can detect rendering errors and apply appropriate countermeasures when required. Classical solutions such as Cyclic Redundancy Checks (CRC) will soon be no longer viable as any sort of alpha blending, warping of scaling of content can cause unwanted CRC violations. Therefore, we propose a novel monitoring approach to verify correctness of displayed content using telltales (e.g. warning signs) as example. It uses a learning-based approach to separate "good" telltales, i.e. those that a human driver will understand correctly, and "corrupted" telltales, i.e. those that will not be visible or perceived correctly. As a result, it possesses inherent resilience against individual pixel errors and implicitly supports changing backgrounds, overlay or scaling effects. This is underlined by our experimental study where all "corrupted" test patterns were correctly classified, while no false alarms were triggered.
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