论文标题
使用机器学习来检测汽车雷达中的幽灵图像
Using Machine Learning to Detect Ghost Images in Automotive Radar
论文作者
论文摘要
雷达传感器是驾驶员辅助系统和智能车辆的重要组成部分，因为它们在各种不良条件下，例如雾，雪，雨，甚至是直射的阳光。与摄像机或激光镜等光基传感器相比，这种鲁棒性是通过实质上更大的波长实现的。作为副作用，许多表面在此波长下像镜子一样起作用，从而导致不必要的幽灵检测。在本文中，我们提出了一种新颖的方法来通过应用数据驱动的机器学习算法来检测这些鬼对象。为此，我们使用带有带注释的幽灵对象的大规模汽车数据集。我们表明，我们可以使用最先进的汽车雷达分类器，以便与真实对象一起检测鬼对象。此外，我们能够减少某些设置中幽灵图像引起的假阳性检测的量。
Radar sensors are an important part of driver assistance systems and intelligent vehicles due to their robustness against all kinds of adverse conditions, e.g., fog, snow, rain, or even direct sunlight. This robustness is achieved by a substantially larger wavelength compared to light-based sensors such as cameras or lidars. As a side effect, many surfaces act like mirrors at this wavelength, resulting in unwanted ghost detections. In this article, we present a novel approach to detect these ghost objects by applying data-driven machine learning algorithms. For this purpose, we use a large-scale automotive data set with annotated ghost objects. We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects. Furthermore, we are able to reduce the amount of false positive detections caused by ghost images in some settings.
