Using Machine Learning to Detect Ghost Images in Automotive Radar
This addresses a specific issue in automotive safety systems for driver assistance and intelligent vehicles, but it is incremental as it applies existing methods to a new data type.
The paper tackles the problem of ghost detections in automotive radar caused by reflective surfaces, presenting a machine learning approach that uses a state-of-the-art classifier on a large annotated dataset to detect ghost objects alongside real ones and reduce false positives in some settings.
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.