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Artech House USA
Radar for Fully Autonomous Driving

Radar for Fully Autonomous Driving

By (author): Matt Markel
Copyright: 2022
Pages: 360
ISBN: 9781630818968

Artech House is pleased to offer you this title in a special In-Print-Forever® ( IPF® ) hardbound edition. This book is not available from inventory but can be printed at your request and delivered within 2-4 weeks of receipt of order. Please note that because IPF® books are printed on demand, returns cannot be accepted.

Hardback $170.00 Qty:
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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.

Part 1: Radar Technologies for Autonomous Vehicles
1 Modern Radar Sensors in Advanced Automotive Architectures

1.1 Inspiration for more advanced systems
1.1.1 Traffic density, fatal accident rate
1.1.2 Human factor
1.1.3 Autonomous driving levels
1.2 The Evolving Automotive Radar Landscape
1.3 Fast Chirp Sequence Radar Sensing
1.4 RFCMOS Car Radar Transceiver
1.5 Elements of a Radar Module
1.6 Angular Resolution Increase: MIMO Example and Cascaded Application
1.7 Vehicle Network and Computer Considerations
1.7.1 Vehicle network architecture evolution
1.7.2 Distributed vs Centralized processing
1.7.3 Conclusion
1.8 Summary
1.9 Acknowledgements
2 Design Considerations for Automotive Radar
2.1 Radar Requirements
2.2 The Spectrum for Automotive Radar
2.3 Range (distance) Required for Automotive Radar
2.4 Automotive Radar Installation
2.5 Automotive Radar Considerations for Scanning the Field of View
2.6 Frequency Modulation Waveforms and the Radar Data Cube
2.7 Outputs from Automotive Radar
3 DCM - Digital Code Modulation – Radar
3.1 Introduction
3.2 FCM vs DCM architecture
3.3 Basics of DCM Radar
3.3.1 Range processing
3.3.2 Velocity processing
3.3.3 Angle Processing
3.4 DCM Radar Attributes
3.4.1 High contrast Distance – Matched filter
3.4.2 High-contrast resolution
3.4.3 CDM MIMO (higher power on target)
3.4.4 Interference robustness and mitigation
3.4.5 Cascading: Coherent and Quasi-coherent Sensors and Networks
3.4.6 Code design
3.5 DCM Radar Implementation
4 Automotive MIMO Radar
4.1 Virtual Array Synthesis via MIMO Radar
4.2 Waveform Orthogonality Strategies in Automotive MIMO Radar
4.2.1 Waveform orthogonality via time division multiplexing (TDM)
4.2.2 Waveform orthogonality via Doppler division multiplexing (DDM)
4.2.3 Waveform orthogonality via frequency division multiplexing (FDM)
4.3 Angle Finding in Automotive MIMO Radar
4.3.1 High resolution angle finding with uniform linear array (ULA) Subspace methods with spatial smoothing Compressive sensing Iterative adaptive approach (IAA)
4.3.2 High resolution angle finding with sparse linear array (SLA)
4.4 High Resolution Imaging Radar for Autonomous Driving
4.4.1 Cascade of multiple radar transceivers
4.4.2 Examples of cascaded imaging radars
4.4.3 Design challenges of imaging radar
4.5 Challenges in Automotive MIMO radar
4.5.1 Angle finding in the presence of multipath reflections
4.5.2 Waveform orthogonality in automotive MIMO radar
4.5.3 Efficient, high resolution angle finding algorithms are needed
5 Synthetic Aperture Radar for Automotive Applications
5.1 Introduction
5.1.1 Historical Background
5.1.2 Comparison to Traditional Radar System Phased array analogy
5.1.3 SAR and Point Cloud Imaging Performance
5.1.4 Applications for Automotive use Drivable-Area Detection Radar-based Localization Segmentation and Classification Radar Point Cloud Enhancement
5.2 Mathematical Foundation
5.2.1 Key Assumptions
5.2.2 Signal Model
5.2.3 Slow Time
5.3 Building an Automotive SAR
5.3.1 Measuring Ego-Motion Positioning Accuracy Requirements Positioning Sensors
5.3.2 SAR Image Formation Time-Domain Back Projection Fast Back-Projection (FBP) Fast Factorized Back-Projection (FFBP) Real-time Operation
5.3.3 Coexistence with Point Cloud Pipeline
5.3.4 Elevation Information
5.4 Future Directions
5.4.1 Forward-facing SAR
5.4.2 SAR for Moving Objects
5.4.3 Gapped SAR
5.5 Conclusion
6 Radar Transceiver Technologies
6.1 Background and Introduction to Automotive Radar
6.2 Block Diagram Overview of an FMCW Radar Transceiver
6.3 Challenges with Deeply Scaled CMOS
6.4 Active devices in CMOS
6.5 Passives in CMOS
6.6 Circuit Architectures suitable for Advanced CMOS
6.6.1 The Transmit Power Amplifier
6.6.2 The TX Phase Shifter
6.7 The LO / FMCW Chirp Generator
6.8 The Receiver Signal Chain
6.8.1 RX Front End
6.8.2 Radar RX Baseband
6.9 Summary
7 Radar Challenges from the Automotive Scene
7.1 Introduction
7.1.1 Range Swath
7.1.2 Imaging dense clutter
7.1.3 Simultaneous Transmit and Receive
7.2 Scene Dynamic Range
7.3 Ground Bounce (Unresolved Reflections)
7.4 Multipath (Resolved Reflections)
8 Radar Interference
8.1 Introduction
8.2 Motivation and Definitions
8.3 Impacts and Manifestation
8.3.1 Linear Frequency Modulation (LFM)/Frequency Modulated Continuous Wave (FMCW)
8.3.2 Phase modulated continuous wave (PMCW) radar and Mixed waveforms FMCW interfering with PMCW PMCM to FMCW PMCW to PMCW
8.4 RFI Mitigations
8.4.1 Mitigations Local to the Radar
8.4.2 Global Mitigations: Non-Cooperative Countermeasures
8.4.3 Global Mitigations: Cooperative Countermeasures
8.4.4 Global Mitigations: Regulations
8.5 Recommendations for the future
8.5.1 Use Less Energy and Power
8.5.2 Report confidence
8.5.3 Create a useful taxonomy for RFI mitigation
9 The Impacts of Water (Weather) on Automotive Radar
9.1 Introduction
9.2 System Losses
9.2.1 Transmission Loss
9.2.2 Target Loss
9.2.3 Radome Loss
9.3 Array Performance
9.4 Backscattering Phenomenology
9.4.1 Rainfall Backscatterer
9.4.2 Road Spray
9.5 Potential Mitigations
Part 3: Integration and System Considerations
10 Safety Considerations for Radars in Fully Autonomous Vehicles
10.1 Introduction
10.2 What is Safety?
10.3 Safety Standards
10.3.1 ISO 26262 and ISO 21448
10.3.2 Relationship to Existing Standards and Processes
10.4 Lessons from Industry
10.4.1 Emphasize understanding over following checklists
10.4.2 Embrace systems engineering
10.4.3 Address safety in the most appropriate place
10.4.4 Improve Supplier / Customer Engagement
10.4.5 Recognize the criticality of a high-quality Safety Manual
10.4.6 Beware the many pitfalls of safety analysis
10.4.7 Applying Safety to emerging or complex technologies
10.5 Safety Concepts for L4 ADS and Implications for Radar
10.5.1 Safety Considerations on Multiple Sensor Modalities
10.5.2 Safety Considerations on Radar Data
10.5.3 Radar FuSa and SOTIF Roots Causes and Mitigations
10.5.4 Safety Considerations Due to Available Radar Technology
10.6 Safety Consideration for Verification & Validation
10.7 Conclusion
11 Testing Automotive Radars
11.1 Introduction - Why is testing necessary?
11.1.1 Validation and verification of system performance
11.1.2 Conformance to legal regulations and industrial standards
11.1.3 Safety performance assessment
11.2 Measurable Parameters - From sensor level to vehicle integration
11.2.1 Transmitter Tests
11.2.2 Receiver Test
11.2.3 Antenna and Radome Test
11.2.4 Performance and Functional Tests
11.2.5 Integration Testing
11.3 Test Equipment
11.3.1 General Test Equipment
11.3.2 Radar Echo Generators
11.3.3 Measurement antennas
11.3.4 Anechoic chambers
11.3.5 Positioners
11.4 Example Test Setups
11.4.1 Transmitter Test Setup
11.4.2 Setup for sensor calibration and performance tests
11.4.3 Setups for EMC and OOB testing
11.4.4 Simulating interference from other automotive radar transmitters
11.4.5 Exemplary test scenario
11.4.6 ADAS Integration Testbed
11.4.7 Vehicle-in-the-Loop Test
Dr. Markel Bio
Contributor Bios

  • Matt Markel

    is an engineering leader with more than 30 years of experience in the development of advanced technologies, specifically in the fields of radar and electronic warfare for both defense and commercial applications. He was a Principal Engineering Fellow for Raytheon, directing multiple programs that advanced the capability, effectiveness, autonomy, and cognition in U.S. systems. Recently he led the radar team at Waymo, formerly the Google Self-Driving Car Project. He has a Ph.D. from the University of Florida.


    Contributors to this volume include:


    Mark Steigemann

    Cicero S. Vaucher

    Shunqiao Sun

    Donnie Smith

    Ralf Reuter

    Arunesh Roy

    Antonio Puglielli

    Mike Keaveney

    Gary Clayton

    Tim Campbell

    Michael Fina

    Ryan Fan

    Xiaoli Lu

    James McGinley

    Leander Humbert

    Jimmy Wang

    Griffin Foster

    Darsh Ranjan

    Vinayak Nagpal

    Yaohui Liu

    Murtaza Ali

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