2 research outputs found
Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
Baseline generation for tracking applications is a difficult task when
working with real world radar data. Data sparsity usually only allows an
indirect way of estimating the original tracks as most objects' centers are not
represented in the data. This article proposes an automated way of acquiring
reference trajectories by using a highly accurate hand-held global navigation
satellite system (GNSS). An embedded inertial measurement unit (IMU) is used
for estimating orientation and motion behavior. This article contains two major
contributions. A method for associating radar data to vulnerable road user
(VRU) tracks is described. It is evaluated how accurate the system performs
under different GNSS reception conditions and how carrying a reference system
alters radar measurements. Second, the system is used to track pedestrians and
cyclists over many measurement cycles in order to generate object centered
occupancy grid maps. The reference system allows to much more precisely
generate real world radar data distributions of VRUs than compared to
conventional methods. Hereby, an important step towards radar-based VRU
tracking is accomplished.Comment: 10 pages, 9 figures, accepted paper for 2019 20th International Radar
Symposium (IRS), Ulm, Germany, June 2019. arXiv admin note: text overlap with
arXiv:1905.1121
Using Machine Learning to Detect Ghost Images in Automotive Radar
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