18 research outputs found
Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles
Radar-based road user classification is an important yet still challenging
task towards autonomous driving applications. The resolution of conventional
automotive radar sensors results in a sparse data representation which is tough
to recover by subsequent signal processing. In this article, classifier
ensembles originating from a one-vs-one binarization paradigm are enriched by
one-vs-all correction classifiers. They are utilized to efficiently classify
individual traffic participants and also identify hidden object classes which
have not been presented to the classifiers during training. For each classifier
of the ensemble an individual feature set is determined from a total set of 98
features. Thereby, the overall classification performance can be improved when
compared to previous methods and, additionally, novel classes can be identified
much more accurately. Furthermore, the proposed structure allows to give new
insights in the importance of features for the recognition of individual
classes which is crucial for the development of new algorithms and sensor
requirements.Comment: 8 pages, 9 figures, accepted paper for 2019 IEEE Intelligent Vehicles
Symposium (IV), Paris, France, June 201
Radar-based Feature Design and Multiclass Classification for Road User Recognition
The classification of individual traffic participants is a complex task,
especially for challenging scenarios with multiple road users or under bad
weather conditions. Radar sensors provide an - with respect to well established
camera systems - orthogonal way of measuring such scenes. In order to gain
accurate classification results, 50 different features are extracted from the
measurement data and tested on their performance. From these features a
suitable subset is chosen and passed to random forest and long short-term
memory (LSTM) classifiers to obtain class predictions for the radar input.
Moreover, it is shown why data imbalance is an inherent problem in automotive
radar classification when the dataset is not sufficiently large. To overcome
this issue, classifier binarization is used among other techniques in order to
better account for underrepresented classes. A new method to couple the
resulting probabilities is proposed and compared to others with great success.
Final results show substantial improvements when compared to ordinary
multiclass classificationComment: 8 pages, 6 figure
A Multi-Stage Clustering Framework for Automotive Radar Data
Radar sensors provide a unique method for executing environmental perception
tasks towards autonomous driving. Especially their capability to perform well
in adverse weather conditions often makes them superior to other sensors such
as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of
the commonly used detection data level is a major challenge for subsequent
signal processing. Therefore, the data points are often merged in order to form
larger entities from which more information can be gathered. The merging
process is often implemented in form of a clustering algorithm. This article
describes a novel approach for first filtering out static background data
before applying a twostage clustering approach. The two-stage clustering
follows the same paradigm as the idea for data association itself: First,
clustering what is ought to belong together in a low dimensional parameter
space, then, extracting additional features from the newly created clusters in
order to perform a final clustering step. Parameters are optimized for
filtering and both clustering steps. All techniques are assessed both
individually and as a whole in order to demonstrate their effectiveness. Final
results indicate clear benefits of the first two methods and also the cluster
merging process under specific circumstances.Comment: 8 pages, 5 figures, accepted paper for 2019 IEEE 22nd Intelligent
Transportation Systems Conference (ITSC), Auckland, New Zealand, October 201
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
A Vehicular Environment Perception Platform for Safety Related Applications
AbstractThe aim of this paper is to present a perception platform developed for vehicular safety applications. The work described here is part of the work carried out in the interactIVe project and more specifically inside the PERCEPTION sub project. InteractIVe is a large scale integrating project co-funded by the European Commission as part of the FP7-ICT for Safety and Energy Efficiency in Mobility. One of the main objectives of this project is the implementation of a reference perception platform with a general purpose interface to the applications
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
Conventional sensor systems record information about directly visible
objects, whereas occluded scene components are considered lost in the
measurement process. Non-line-of-sight (NLOS) methods try to recover such
hidden objects from their indirect reflections - faint signal components,
traditionally treated as measurement noise. Existing NLOS approaches struggle
to record these low-signal components outside the lab, and do not scale to
large-scale outdoor scenes and high-speed motion, typical in automotive
scenarios. In particular, optical NLOS capture is fundamentally limited by the
quartic intensity falloff of diffuse indirect reflections. In this work, we
depart from visible-wavelength approaches and demonstrate detection,
classification, and tracking of hidden objects in large-scale dynamic
environments using Doppler radars that can be manufactured at low-cost in
series production. To untangle noisy indirect and direct reflections, we learn
from temporal sequences of Doppler velocity and position measurements, which we
fuse in a joint NLOS detection and tracking network over time. We validate the
approach on in-the-wild automotive scenes, including sequences of parked cars
or house facades as relay surfaces, and demonstrate low-cost, real-time NLOS in
dynamic automotive environments.Comment: First three authors contributed equally; Accepted at CVPR 202
Results of a Precrash Application Based on Laser Scanner and Short-Range Radars
International audienceIn this paper, we present a vehicle safety application based on data gathered by a laser scanner and two short-range radars that recognize unavoidable collisions with stationary objects before they take place to trigger restraint systems. Two different software modules that perform the processing of raw data and deliver a description of the vehicle's environment are compared. A comprehensive experimental evaluation based on relevant crash and noncrash scenarios is presented
Online Localization and Mapping with Moving Object Tracking in Dynamic Outdoor Environments
International audienceIn this paper, we present a real-time algorithm for online simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) in dynamic outdoor environments from a moving vehicle equipped with laser sensor and odometry. To correct vehicle location from odometry we introduce a new fast implementation of incremental scan matching method that can work reliably in dynamic outdoor environments. After a good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Detected moving objects are finally tracked using Global Nearest Neighborhood (GNN) method. The experimental results on datasets collected from different scenarios such as: urban streets, country roads and highways demonstrate the efficiency of the proposed algorithm
Enhancement of doppler unambiguity for chirp-sequence modulated TDM-MIMO radars
Current automotive radar sensors enhance the angular resolution using a multiple-input multiple-output approach. The often applied time-division multiplexing scheme has the drawback of a reduced unambiguous Doppler velocity proportional to the number of transmitters. In this paper, a signal processing scheme is proposed to regain the same unambiguous Doppler as in the single-input multiple-output case with only one transmit antenna. Simulation and measurement results are shown to prove that the signal processing leads to an enhanced unambiguous Doppler velocity estimation