Radar for Fully Autonomous Driving

Radar for Fully Autonomous Driving

Author: Matt Markel

Publisher: Artech House

Published: 2022-04-30

Total Pages: 360

ISBN-13: 1630818976

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This is the first book to bring together the increasingly complex radar automotive technologies and tools being explored and utilized in the development of fully autonomous vehicles – technologies and tools now understood to be an essential need for the field to fully mature. The book presents state-of-the-art knowledge as shared by the best and brightest experts working in the automotive radar industry today -- leaders who have “been there and done that.” Each chapter is written as a standalone "master class" with the authors, seeing the topic through their eyes and experiences. Where beneficial, the chapters reference one another but can otherwise be read in any order desired, making the book an excellent go-to reference for a particular topic or review you need to understand. You’ll get a big-picture tour of the key radar needs for fully autonomous vehicles, and how achieving these needs is complicated by the automotive environment’s dense scenes, number of possible targets of interest, and mix of very large and very small returns. You’ll then be shown the challenges from – and mitigations to – radio frequency interference (RFI), an ever-increasing challenge as the number of vehicles with radars – and radars per vehicle grow. The book also dives into the impacts of weather on radar performance, providing you with insights gained from extensive real-world testing. You are then taken through the integration and systems considerations, especially regarding safety, computing needs, and testing. Each of these areas is influenced heavily by the needs of fully autonomous vehicles and are open areas of research and development. With this authoritative volume you will understand: * How to engage with radar designers (from a system integrator / OEM standpoint); * How to structure and set requirements for automotive radars; * How to address system safety needs for radars in fully autonomous vehicles; * How to assess weather impact on the radar and its ability to support autonomy; * How to include weather effects into specifications for radars. This is an essential reference for engineers currently in the autonomous vehicle arena and/or working in automotive radar development, as well as engineers and leaders in adjacent radar fields needing to stay abreast of the rapid developments in this exciting and dynamic field of research and development.


Large Aperture Array Radar Systems for Automotive Applications

Large Aperture Array Radar Systems for Automotive Applications

Author: Fabian Schwartau

Publisher: Cuvillier Verlag

Published: 2021-10-18

Total Pages: 144

ISBN-13: 3736965079

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The radar, besides camera and Lidar systems, is a core sensor to enable autonomous driving. The relatively limited angular resolution is the major drawback of the radar. This thesis shows the development of a conceptual future radar system for automotive applications. The focus is on providing a large antenna aperture to achieve the required high angular resolution. Two genetic algorithms are developed to optimize the antenna array for a good side lobe level while providing high angular resolution. Two demonstrators are built to implement certain aspects of the proposed radar system and prove the general concept viable. The first demonstrator features a large aperture with a limited side lobe level and is using a modular approach. The modules are synchronized with a radio over fiber system. The second demonstrator uses the previously proposed antenna array, which is implemented with a synthetic aperture radar approach. The system’s capabilities in a real scenario are demonstrated, and the reconstruction of a high-resolution three-dimensional image from the captured data is shown. Das Radar stellt, neben Kamera- und Lidar-Systemen, einen zentralen Sensor für das autonome Fahren dar. Dabei ist die relativ geringe Winelauflösung der primäre Nachteil des Radars. Diese Arbeit zeigt die Entwicklung eines konzeptionellen zukünftigen Radarsystems für automobile Anwendungen. Der Schwerpunkt liegt auf der Umsetzung einer großen Antennenapertur, um die erforderliche hohe Winkelauflösung zu erreichen. Zwei evolutionäre Algorithmen werden vorgestellt, um das Antennen-Array auf einen guten Nebenkeulen-Pegel zu optimieren und gleichzeitig eine hohe Winkelauflösung zu erreichen. Zwei Demonstratoren werden gebaut, um bestimmte Aspekte des vorgeschlagenen Radarsystems zu implementieren und die Durchführbarkeit des allgemeinen Konzepts zu zeigen. Der erste Demonstrator weist eine große Apertur mit einem begrenzten Nebenkeulen-Niveau auf und verwendet einen modularen Ansatz. Die Module sind mit einem Radio-over-Fiber-System synchronisiert. Der zweite Demonstrator verwendet die zuvor entworfene Antennenanordnung, die mit einem Radar mit synthetischer Apertur realisiert wird. Die Fähigkeiten des Systems werden in einem realen Szenario demonstriert und die Rekonstruktion eines hochauflösenden dreidimensionalen Bildes aus den erfassten Daten gezeigt.


Radar Signal Processing for Autonomous Driving

Radar Signal Processing for Autonomous Driving

Author: Jonah Gamba

Publisher: Springer

Published: 2019-08-02

Total Pages: 142

ISBN-13: 9811391939

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The subject of this book is theory, principles and methods used in radar algorithm development with a special focus on automotive radar signal processing. In the automotive industry, autonomous driving is currently a hot topic that leads to numerous applications for both safety and driving comfort. It is estimated that full autonomous driving will be realized in the next twenty to thirty years and one of the enabling technologies is radar sensing. This book presents both detection and tracking topics specifically for automotive radar processing. It provides illustrations, figures and tables for the reader to quickly grasp the concepts and start working on practical solutions. The complete and comprehensive coverage of the topic provides both professionals and newcomers with all the essential methods and tools required to successfully implement and evaluate automotive radar processing algorithms.


High-Precision Automotive Radar Target Simulation

High-Precision Automotive Radar Target Simulation

Author: Diewald, Axel

Publisher: KIT Scientific Publishing

Published: 2023-08-15

Total Pages: 190

ISBN-13: 3731512963

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Radar target simulators (RTSs) deceive a radar under test (RuT) by creating an artificial environment consisting of virtual radar targets. In this work, new techniques are presented that overcome the rasterization deficiency of current RTS systems and enable the generation of virtual targets at arbitrary high-precision positions. This allows for continuous movement of the targets and thus a more credible simulation environment.


Radar Signal Processing for Autonomous Driving

Radar Signal Processing for Autonomous Driving

Author: Jonah Gamba

Publisher:

Published: 2020

Total Pages:

ISBN-13: 9789811391941

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The subject of this book is theory, principles and methods used in radar algorithm development with a special focus on automotive radar signal processing. In the automotive industry, autonomous driving is currently a hot topic that leads to numerous applications for both safety and driving comfort. It is estimated that full autonomous driving will be realized in the next twenty to thirty years and one of the enabling technologies is radar sensing. This book presents both detection and tracking topics specifically for automotive radar processing. It provides illustrations, figures and tables for the reader to quickly grasp the concepts and start working on practical solutions. The complete and comprehensive coverage of the topic provides both professionals and newcomers with all the essential methods and tools required to successfully implement and evaluate automotive radar processing algorithms.


Automotive Radar Sensors in Silicon Technologies

Automotive Radar Sensors in Silicon Technologies

Author: Vipul Jain

Publisher: Springer Science & Business Media

Published: 2012-09-26

Total Pages: 102

ISBN-13: 1441967753

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One of the leading causes of automobile accidents is the slow reaction of the driver while responding to a hazardous situation. State-of-the-art wireless electronics can automate several driving functions, leading to significant reduction in human error and improvement in vehicle safety. With continuous transistor scaling, silicon fabrication technology now has the potential to substantially reduce the cost of automotive radar sensors. This book bridges an existing gap between information available on dependable system/architecture design and circuit design. It provides the background of the field and detailed description of recent research and development of silicon-based radar sensors. System-level requirements and circuit topologies for radar transceivers are described in detail. Holistic approaches towards designing radar sensors are validated with several examples of highly-integrated radar ICs in silicon technologies. Circuit techniques to design millimeter-wave circuits in silicon technologies are discussed in depth.


Deep Learning Methods for Automotive Radar Signal Processing

Deep Learning Methods for Automotive Radar Signal Processing

Author: Rodrigo Pérez González

Publisher: Cuvillier Verlag

Published: 2021-06-28

Total Pages: 136

ISBN-13: 3736964625

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Um autonomes Fahren zu ermöglichen, müssen zukünftige Sensorsysteme nicht nur in der Lage sein, das Fahrumfeld zu erfassen, sondern auch semantische Informationen zu liefern. In dieser Arbeit werden Deep Learning Methoden, die die klassische Radarsignalverarbeitungskette verbessern oder sogar ersetzen sollen, entwickelt und im Hinblick auf das Automobilumfeld evaluiert. Für diesen Zweck werden hochmoderne Bilderkennungsalgorithmen auf die Domäne der Radarsignale angepasst und zur Klassifizierung und Detektion verschiedener Verkehrsteilnehmer angewendet. For autonomous driving to become a reality, future sensor systems must be able to not only capture the vehicle’s environment, but also to provide semantic information. In this work, deep learning methods, meant to enhance—or even replace—the classical radar signal processing chain, are developed and evaluated in the context of automotive applications. For this purpose, state of the art computer vision approaches are adapted and applied to radar signals in order to detect and classify different road users.


Highly Integrated Low Power Radars

Highly Integrated Low Power Radars

Author: Sergio Saponara

Publisher: Artech House

Published: 2014-06-01

Total Pages: 231

ISBN-13: 1608076652

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In recent years, advances in radio detection and ranging technology, sustained by new achievements in the fields of signal processing and electronic components, have permitted the adoption of radars in many civil and defense applications. This resource discusses how highly integrated radar has been adopted by several new markets such as contactless vital sign monitoring (heart rate, breath rate) or harbour traffic control, as well as several applications for vehicle driver assistance. You are provided with scenarios, applications, and requirements, while focusing on the trade-offs between flexibility, programmability, power consumption, size and weight, and complexity.


Polarimetric Radar for Automotive Applications

Polarimetric Radar for Automotive Applications

Author: Tristan Visentin

Publisher: Saint Philip Street Press

Published: 2020-10-09

Total Pages: 0

ISBN-13: 9781013283420

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Current automotive radar sensors prove to be a weather robust and low-cost solution, but are suffering from low resolution and are not capable of classifying detected targets. However, for future applications like autonomous driving, such features are becoming ever increasingly important. On the basis of successful state-of-the-art applications, this work presents the first in-depth analysis and ground-breaking, novel results of polarimetric millimeter wave radars for automotive applications. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.