188 research outputs found
Autonomous Vehicles: MMW Radar Backscattering Modeling of Traffic Environment, Vehicular Communication Modeling, and Antenna Designs
77 GHz Millimeter-wave (mmWave) radar serves as an essential component among many sensors required for autonomous navigation. High-fidelity simulation is indispensable for nowadays’ development of advanced automotive radar systems because radar simulation can accelerate the design and testing process and help people to better understand and process the radar data. The main challenge in automotive radar simulation is to simulate the complex scattering behavior of various targets in real time, which is required for sensor fusion with other sensory simulation, e.g. optical image simulation.
In this thesis, an asymptotic method based on a fast-wideband physical optics (PO) calculation is developed and applied to get high fidelity radar response of traffic scenes and generate the corresponding radar images from traffic targets. The targets include pedestrians, vehicles, and other stationary targets. To further accelerate the simulation into real time, a physics-based statistical approach is developed. The RCS of targets are fit into statistical distributions, and then the statistical parameters are summarized as functions of range and aspect angles, and other attributes of the targets. For advanced radar with multiple transmitters and receivers, pixelated-scatterer statistical RCS models are developed to represent objects as extend targets and relax the requirement for far-field condition. A real-time radar scene simulation software, which will be referred to as Michigan Automotive Radar Scene Simulator (MARSS), based on the statistical models are developed and integrated with a physical 3D scene generation software (Unreal Engine 4). One of the major challenges in radar signal processing is to detect the angle of arrival (AOA) of multiple targets. A new analytic multiple-sources AOA estimation algorithm that outperforms many well-known AOA estimation algorithms is developed and verified by experiments. Moreover, the statistical parameters of RCS from targets and radar images are used in target classification approaches based on machine learning methods.
In realistic road traffic environment, foliage is commonly encountered that can potentially block the line-of-sight link. In the second part of the thesis, a non-line-of-sight (NLoS) vehicular propagation channel model for tree trunks at two vehicular communication bands (5.9 GHz and 60 GHz) is proposed. Both near-field and far-field scattering models from tree trunk are developed based on modal expansion and surface current integral method. To make the results fast accessible and retractable, a macro model based on artificial neural network (ANN) is proposed to fit the path loss calculated from the complex electromagnetic (EM) based methods.
In the third part of the thesis, two broadband (bandwidth > 50%) omnidirectional antenna designs are discussed to enable polarization diversity for next-generation communication systems. The first design is a compact horizontally polarized (HP) antenna, which contains four folded dipole radiators and utilizing their mutual coupling to enhance the bandwidth. The second one is a circularly polarized (CP) antenna. It is composed of one ultra-wide-band (UWB) monopole, the compact HP antenna, and a dedicatedly designed asymmetric power divider based feeding network. It has about 53% overlapping bandwidth for both impedance and axial ratio with peak RHCP gain of 0.9 dBi.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163001/1/caixz_1.pd
Motion Estimation and Compensation in Automotive MIMO SAR
With the advent of self-driving vehicles, autonomous driving systems will
have to rely on a vast number of heterogeneous sensors to perform dynamic
perception of the surrounding environment. Synthetic Aperture Radar (SAR)
systems increase the resolution of conventional mass-market radars by
exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the
trajectory, usually not compatible with automotive-grade navigation systems. In
this regard, this paper deals with the analysis, estimation and compensation of
trajectory estimation errors in automotive SAR systems, proposing a complete
residual motion estimation and compensation workflow. We start by defining the
geometry of the acquisition and the basic processing steps of Multiple-Input
Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of
typical motion errors in automotive SAR imaging. Based on the derived models,
the procedure is detailed, outlining the guidelines for its practical
implementation. We show the effectiveness of the proposed technique by means of
experimental data gathered by a 77 GHz radar mounted in a forward looking
configuration.Comment: 14 page
Machine learning applied to radar data: classification and semantic instance segmentation of moving road users
Classification and semantic instance segmentation applications are rarely considered for automotive radar sensors. In current implementations, objects have to be
tracked over time before some semantic information can be extracted. In this thesis,
data from a network of 77 GHz automotive radar sensors is used to construct, train
and evaluate machine learning algorithms for the classification of moving road
users. The classification step is deliberately performed early in the process chain so
that a subsequent tracking algorithm can benefit from this extra information. For
this purpose, a large data set with real-world scenarios from about 5 h of driving
was recorded and annotated.
Given that the point clouds measured by the radar sensors are both sparse and
noisy, the proposed methods have to be sensitive to those features that discern the
individual classes from each other and at the same time, they have to be robust to
outliers and measurement errors. Two groups of applications are considered: classi-
fication of clustered data and semantic (instance) segmentation of whole scenes.
In the first category, specifically designed density-based clustering algorithms are
used to group individual measurements to objects. These objects are then used
either as input to a manual feature extraction step or as input to a neural network,
which operates directly on the bare input points. Different classifiers are trained
and evaluated on these input data.
For the algorithms of the second category, the measurements of a whole scene
are used as input, so that the clustering step becomes obsolete. A newly designed
recurrent neural network for instance segmentation of point clouds is utilized. This
approach outperforms all of the other proposed methods and exceeds the baseline
score by about ten percentage points.
In additional experiments, the performance of human test candidates on the same
task is analyzed. This study shows that temporal correlations in the data are of
great use for the test candidates, who are nevertheless outrun by the recurrent
network
Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems
The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system
Millimetre-wave radar development for high resolution detection
Automotive technology today is focusing on autonomous vehicle development. The
sensors for these systems include radars due to their robustness against adverse
weather conditions such as rain, fog, ash or snow. In this constant search for advancement, high resolution systems play a central role in target detection and avoidance. In this PhD project, these methods have been researched and engineered to
leverage the best radar resolution for collision avoidance systems.
The first part of this thesis will focus on the existing systems consisting of the
state-of-the-art at the time of writing and explain what makes a high resolution
radar and how it can cover the whole field of view. The second part will focus on
how a non-uniform sparse radar system was simulated, developed and benchmarked
for improved radar performance up to 40% better than conventional designs. The
third part will focus on signal processing techniques and how these methods have
achieved high resolution and detection: large virtual aperture array using Multiple
Input Multiple Output (MIMO) systems, beampattern multiplication to improve
side-lobe levels and compressive sensing. Also, the substrate-integrated waveguide
(SIW) antennas which have been fabricated provide a bandwidth of 1.5GHz for the
transmitter and 2GHz at the receiver. This has resulted in a range resolution of 10
cm. The four part of this thesis presents the measurements which have been carried
out at the facilities within Heriot-Watt University and also at Netherlands Organisation for Applied Scientific Research (TNO). The results were better than expected
since a two transmitter four receiver system was able to detect targets which have
been separated at 2.2◦
in angle in the horizontal plane. Also, compressive sensing was used as a high resolution method for obtaining fine target detection and
in combination with the multiplication method showed improved detection performance with a 20 dB side-lobe level suppression. The measurement results from the
6-months placements are presented and compared with the state-of the art, revealing that the developed radar is comparable in performance to high-grade automotive
radars developed in the industry
Towards a Common Software/Hardware Methodology for Future Advanced Driver Assistance Systems
The European research project DESERVE (DEvelopment platform for Safe and Efficient dRiVE, 2012-2015) had the aim of designing and developing a platform tool to cope with the continuously increasing complexity and the simultaneous need to reduce cost for future embedded Advanced Driver Assistance Systems (ADAS). For this purpose, the DESERVE platform profits from cross-domain software reuse, standardization of automotive software component interfaces, and easy but safety-compliant integration of heterogeneous modules. This enables the development of a new generation of ADAS applications, which challengingly combine different functions, sensors, actuators, hardware platforms, and Human Machine Interfaces (HMI). This book presents the different results of the DESERVE project concerning the ADAS development platform, test case functions, and validation and evaluation of different approaches. The reader is invited to substantiate the content of this book with the deliverables published during the DESERVE project. Technical topics discussed in this book include:Modern ADAS development platforms;Design space exploration;Driving modelling;Video-based and Radar-based ADAS functions;HMI for ADAS;Vehicle-hardware-in-the-loop validation system
Sensor Fusion and Resource Management in MIMO-OFDM Joint Sensing and Communication
This study explores the promising potential of integrating sensing
capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency
division multiplexing (OFDM)-based networks through innovative multi-sensor
fusion techniques, tracking algorithms, and resource management. A novel data
fusion technique is proposed within the MIMO-OFDM system, which promotes
cooperative sensing among monostatic joint sensing and communication (JSC) base
stations by sharing range-angle maps with a central fusion center. To manage
data sharing and control network overhead introduced by cooperation, an
excision filter is introduced at each base station. After data fusion, the
framework employs a three-step clustering procedure combined with a tracking
algorithm to effectively handle point-like and extended targets. Delving into
the sensing/communication trade-off, resources such as transmit power,
frequency, and time are varied, providing valuable insights into their impact
on the overall system performance. Additionally, a sophisticated channel model
is proposed, accounting for complex urban propagation scenarios and addressing
multipath effects and multiple reflection points for extended targets like
vehicles. Evaluation metrics, including optimal sub-pattern assignment (OSPA),
downlink sum rate, and bit rate, offer a comprehensive assessment of the
system's localization and communication capabilities, as well as network
overhead
Modeling Backscattering Behavior of Vulnerable Road Users Based on High-Resolution Radar Measurements
Bei der Weiterentwicklung der Technologie des autonomen Fahrens (AD) ist die Beschaffung zuverlässiger dreidimensionaler Umgebungsinformationen eine unverzichtbare Aufgabe, um ein sicheres Fahren zu ermöglichen. Diese Herausforderung kann durch den Einsatz von Fahrzeugradaren zusammen mit optischen Sensoren, z. B. Kameras oder Lidars, bewältigt werden, sei es in der Simulation oder in konventionellen Tests auf der Straße. Das Betriebsverhalten von Fahrzeugradaren kann in einer Over-the-Air (OTA) Vehicle-in-the-Loop (ViL) Umgebung genau bewertet werden. Für eine umfassende experimentelle Verifizierung der Fahrzeugradare muss jedoch die Umgebung, insbesondere die gefährdeten Verkehrsteilnehmer (VRUs), möglichst realistisch modelliert werden. Moderne Radarsensoren sind in der Lage, hochaufgelöste Erkennungsinformationen von komplexen Verkehrszielen zu liefern, um diese zu verfolgen. Diese hochauflösenden Erkennungsdaten, die die reflektierten Signale von den Streupunkten (SPs) der VRUs enthalten, können zur Erzeugung von Rückstreumodelle genutzt werden.
Darüber hinaus kann ein realistischeres Rückstreumodell der VRUs, insbesondere von Menschen als Fußgänger oder Radfahrer, durch die Modellierung der Bewegung ihrer Extremitäten in Verkehrsszenarien erreicht werden. Die Voraussetzung für die Erstellung eines solchen detaillierten Modells in verschiedenen Situationen sind der Radarquerschnitt (RCS) und die Doppler-Signaturen, die sich aus den menschlichen Extremitäten in einer bewegten Situation ergeben. Diese Daten können durch die gesammelten Radardaten aus hochauflösenden RCS-Messungen im Radial- und Winkelbereich gewonnen werden, was durch die Analyse der Range-Doppler-Spezifikation der menschlichen Extremitäten in verschiedenen Bewegungen möglich ist. Die entwickelten realistischen Radarmodelle können bei der Wellenausbreitung im Radarkanal, bei der Zielerkennung und -klassifizierung sowie bei Datentrainingsalgorithmen zur Validierung und Verifizierung der Kfz-Radarfunktionen eingesetzt werden. Anschließend kann mit dieser Bewertung die Sicherheit von fortschrittlichen Fahrerassistenzsystemen (ADAS) beurteilt werden.
Daher wird in dieser Arbeit ein hochauflösendes RCS-Messverfahren vorgeschlagen, um die relevanten SPs verschiedener VRUs mit hoher radialer und winkelmäßiger Auflösung zu bestimmen. Eine Gruppe unterschiedliche VRUs wird in statischen Situationen gemessen, und die notwendigen Signalverarbeitungsschritte, um die relevanten SPs mit den entsprechenden RCS-Werten zu extrahieren, werden im Detail beschrieben. Während der Analyse der gemessenen Daten wird ein Algorithmus entwickelt, um die physischen Größen der gemessenen Testpersonen aus dem extrahierten Rückstreumodell zu schätzen und sie anhand ihrer Größe und Statur zu klassifizieren. Zusätzlich wird ein Dummy-Mensch vermessen, der eine vergleichbare Größe wie die vermessenen Probanden hat. Das extrahierte Rückstreuverhalten einer beispielhaften VRU-Gruppe wird für ihre verschiedenen Typen ausgewertet, um die Übereinstimmung zwischen virtuellen Validierungen und der Realität aufzuzeigen und den Genauigkeitsgrad der Modelle sicherzustellen. In einem weiteren Schritt wird diese hochauflösende RCS-Messtechnik mit der Motion Capture Technologie kombiniert, um die Reflektivität der SPs von den menschlichen Körperregionen in verschiedenen Bewegungen zu erfassen und die Radarsignaturen der menschlichen Extremitäten genau zu schätzen. Spezielle Signalverarbeitungsschritte werden eingesetzt, um die Radarsignaturen aus den Messergebnissen des sich bewegenden Menschen zu extrahieren. Diese nachbearbeiteten Daten ermöglichen es der Technik, die zeitlich variierenden SPs an den Extremitäten des menschlichen Körpers mit den entsprechenden RCS-Werten und Dopplersignaturen einzuführen. Das extrahierte Rückstreumodell der VRUs enthält eine Vielzahl von SPs. Daher wird ein Clustering-Algorithmus entwickelt, um die Berechnungskomplexität bei Radarkanalsimulationen durch die Einführung einiger virtueller Streuzentren (SCs) zu minimieren. Jedes entwickelte virtuelle SCs hat seine eigene spezifische Streueigenschaft
Multi-User Gesture Recognition with Radar Technology
The aim of this work is the development of a Radar system for consumer applications. It is capable of tracking multiple people in a room and offers a touchless human-machine interface for purposes that range from entertainment to hygiene
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