The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions

Bilik, Igal; Longman, Oren; Villeval, Shahar; Tabrikian, Joseph

doi:10.1109/msp.2019.2926573

Cited by 257 publications

(125 citation statements)

References 45 publications

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“…5a, we placed two small targets at different locations to verify the minimum distinguishable range difference. When the range difference was 0.3m, two targets could be separated via Range 1 We intend to release a portion of our dataset which includes the raw radar data and corresponding metadata (labels) for 2 classes of objects (pedestrian and car). Please refer to the link below for more detail.…”

Section: A Radar Dataset Collectionmentioning

confidence: 99%

“…Thereafter, we apply some radar imaging algorithms to the collected dataset, and present some preliminary results that validate its capabilities in terms of object recognition. Over the years, advances in 77GHz RF design with integrated digital CMOS and packaging have enabled low-cost radaron-chip and antenna-on-chip systems [1]. As a result, several vehicular radar vendors are refining their radar chipset solutions for the automotive segment.…”

Section: Introductionmentioning

confidence: 99%

“…However, compared to FMCW radars, PMCW radars shift the modulation complexity/precision to the high-speed dataconverters and the DSP. Overall, continual progress in radar chip designs is expected to enable further novel on-platform integration and, consequently lead to enhanced performance in support of ADAS elements such as adaptive cruise control, auto emergency braking, and lane change assistance [1].…”

Section: Introductionmentioning

confidence: 99%

“…1: (a) FMCW radar test-bed (red board: AWR1642 BOOST; green board: DCA1000 EVM) (b) Vehicle mounted platform for dataset collection II. INTRODUCTIONOver the years, advances in 77GHz RF design with integrated digital CMOS and packaging have enabled low-cost radaron-chip and antenna-on-chip systems [1]. As a result, several vehicular radar vendors are refining their radar chipset solutions for the automotive segment.…”

mentioning

confidence: 99%

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Experiments with mmWave Automotive Radar Test-bed

Gao

Xing

Roy

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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I. ABSTRACTMillimeter-wave (mmW) radars are being increasingly integrated in commercial vehicles to support new Adaptive Driver Assisted Systems (ADAS) for its ability to provide high accuracy location, velocity, and angle estimates of objects, largely independent of environmental conditions. Such radar sensors not only perform basic functions such as detection and ranging/angular localization, but also provide critical inputs for environmental perception via object recognition and classification. To explore radar-based ADAS applications, we have assembled a lab-scale frequency modulated continuous wave (FMCW) radar test-bed (https://depts.washington.edu/funlab/research) based on Texas Instrument's (TI) automotive chipset family. In this work, we describe the test-bed components and provide a summary of FMCW radar operational principles. To date, we have created a large raw radar dataset for various objects under controlled scenarios. Thereafter, we apply some radar imaging algorithms to the collected dataset, and present some preliminary results that validate its capabilities in terms of object recognition. (a) (b) Fig. 1: (a) FMCW radar test-bed (red board: AWR1642 BOOST; green board: DCA1000 EVM) (b) Vehicle mounted platform for dataset collection II. INTRODUCTIONOver the years, advances in 77GHz RF design with integrated digital CMOS and packaging have enabled low-cost radaron-chip and antenna-on-chip systems [1]. As a result, several vehicular radar vendors are refining their radar chipset solutions for the automotive segment. TI's state-of-art 77GHz FMCW radar chips and corresponding evaluation boards -AWR1443, AWR1642, and AWR1843 -are built with the low-power 45nm RF CMOS process and enable unprecedented levels of integration in an extremely small form factor [2]. Uhnder has also recently unveiled a new, all-digital phase modulated continuous wave (PMCW) radar chip that uses the 28nm RF CMOS process and is capable of synthesizing multiple input multiple output (MIMO) radar capability with 192 virtual receivers, thereby obtaining a finer angular resolution [3]. However, compared to FMCW radars, PMCW radars shift the modulation complexity/precision to the high-speed dataconverters and the DSP. Overall, continual progress in radar chip designs is expected to enable further novel on-platform integration and, consequently lead to enhanced performance in support of ADAS elements such as adaptive cruise control, auto emergency braking, and lane change assistance [1].The above applications fundamentally rely on advanced radar imaging, detection, clustering, tracking, and classification algorithms. Significant research in the context of automotive radar classification has demonstrated its feasibility as a good alternative when optical sensors fail to provide adequate performance.[4] reported that with handcrafted feature extraction from range and Doppler profile, over 90% accuracy can be achieved when using the support vector machine (SVM) algorithm to distinguish cars and pedestrians. Other studies used the short...

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Section: A Radar Dataset Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Experiments with mmWave Automotive Radar Test-bed

Gao

Xing

Roy

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

show abstract

“…The ever-increasing requirements for the advanced ADAS systems of the “intelligent” car pose challenges for the solution of which fundamentally different approaches are required. This includes the use of more sophisticated quasi-real-time signal processing algorithms, alternative modulation of sounding radar signals and the development of efficient microwave radar hardware [ 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

An Elaborated Signal Model for Simultaneous Range and Vector Velocity Estimation in FMCW Radar

Ivanov

Kuptsov

Badenko

et al. 2020

Sensors

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A rigorous mathematical description of the signal reflected from a moving object for radar monitoring tasks using linear frequency modulated continuous wave (LFMCW) microwave radars is proposed. The mathematical model is based on the quasi-relativistic vector transformation of coordinates and Lorentz time. The spatio-temporal structure of the echo signal was obtained taking into account the transverse component of the radar target speed, which made it possible to expand the boundaries of the range of measuring the range and speed of vehicles using LFMCW radars. An algorithm for the simultaneous estimation of the range, radial and transverse components of the velocity vector of an object from the observation data of the time series during one frame of the probing signal is proposed. For an automobile 77 GHz microwave LFMCW radar, a computer experiment was carried out to measure the range and velocity vector of a radar target using the developed mathematical model of the echo signal and an algorithm for estimating the motion parameters. The boundaries of the range for measuring the range and speed of the target are determined. The results of the performed computer experiment are in good agreement with the results of theoretical analysis.

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A Review of Autonomous Vehicles

D'Costa,

Ajith

2023

Self‐Powered Cyber Physical Systems

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The Rise of Radar for Autonomous Vehicles: Signal Processing Solutions and Future Research Directions

Cited by 257 publications

References 45 publications

Experiments with mmWave Automotive Radar Test-bed

Experiments with mmWave Automotive Radar Test-bed

An Elaborated Signal Model for Simultaneous Range and Vector Velocity Estimation in FMCW Radar

A Review of Autonomous Vehicles

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