论文标题
演示摘要：移动机器人上的实时分发检测
Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot
论文作者
论文摘要
在网络物理系统（例如自动驾驶汽车（AV））中，可以使用机器学习（ML）模型来导航和识别可能干扰车辆操作的对象。但是，在培训分配以外的数据显示时，ML模型不太可能做出准确的决策。分布（OOD）检测可以通过在运行时识别此类样本来充当ML模型的安全监视器。但是，在像AV这样的安全关键系统中，除功能要求外，OOD检测还需要满足实时限制。在此演示中，我们使用移动机器人作为AV的替代物，并使用OOD检测器来识别潜在的危险样本。机器人使用图像数据和Yolo对象检测网络导航一个微型城镇。我们表明，我们的OOD检测器能够在嵌入式平台上实时识别OOD图像，并同时执行对象检测和泳道。我们还表明，在存在未知的新样品的情况下，它可以用于成功停止车辆。
In a cyber-physical system such as an autonomous vehicle (AV), machine learning (ML) models can be used to navigate and identify objects that may interfere with the vehicle's operation. However, ML models are unlikely to make accurate decisions when presented with data outside their training distribution. Out-of-distribution (OOD) detection can act as a safety monitor for ML models by identifying such samples at run time. However, in safety critical systems like AVs, OOD detection needs to satisfy real-time constraints in addition to functional requirements. In this demonstration, we use a mobile robot as a surrogate for an AV and use an OOD detector to identify potentially hazardous samples. The robot navigates a miniature town using image data and a YOLO object detection network. We show that our OOD detector is capable of identifying OOD images in real-time on an embedded platform concurrently performing object detection and lane following. We also show that it can be used to successfully stop the vehicle in the presence of unknown, novel samples.
