Area-Based CFAR Target Detection for Automotive Millimeter-Wave Radar

Wei, Ziping; Li, Bin; Feng, Tao; Tao, Yiwen; Zhao, Chenglin

doi:10.1109/tvt.2022.3216013

Cited by 11 publications

(4 citation statements)

References 56 publications

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“…Literature [10] presents a technique known as the PreCFAR grid map, designed to combat the issues of sparsity and occlusion in CFAR grid map construction by creating a representation of the static environment. Meanwhile, literature [11] suggests a region-based CFAR approach that utilizes regional target information on a two-dimensional RDM to enhance the detection signal-to-noise ratio, substantially improving CFAR detection efficacy. Additionally, literature [12] proposes a method for adaptive threshold setting, which dynamically adjusts the threshold coefficient of CFAR detection based on the relationship between radar received power and distance.…”

Section: Figure 5 3d Point Cloud Imaging Resultsmentioning

confidence: 99%

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Design of a Multidimensional Intelligent Safety Driving Monitoring System Based on Machine Vision

Song,

Yun,

Wang

et al. 2024

AJST

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This study introduces a multidimensional driving safety monitoring system that integrates millimeter-wave radar and an Arduino Uno-based alcohol sensor. The system aims to enhance driving safety and prevent accidents by real-time monitoring and analysis of the driving environment, driver's condition, and vehicle dynamics using machine vision technology. Utilizing millimeter-wave radar, the system precisely detects obstacles and pedestrians around the vehicle, facilitating efficient distance and speed measurements to support automatic emergency braking and collision warnings. Additionally, the Arduino Uno-based alcohol sensor monitors the driver's breath for alcohol concentration, ensuring the driver is in a suitable state for driving. Combined with machine vision technology, the system further analyzes video stream data from inside and outside the cockpit, monitoring driver behavior and external traffic conditions to enhance the identification of potential risks. The success of this research illustrates the potential of combining machine vision with advanced sensor technologies, offering new insights for the development of comprehensive driving assistance systems.

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Section: Figure 5 3d Point Cloud Imaging Resultsmentioning

confidence: 99%

“…(10) Update the display module to show the alcohol concentration, alarm status, and other relevant information on the LCD screen for driver feedback. (11) Place the entire detection and control process in a loop for ongoing alcohol monitoring and real-time response execution.…”

Section: Principle Of Alcohol Detectionmentioning

confidence: 99%

Design of a Multidimensional Intelligent Safety Driving Monitoring System Based on Machine Vision

Song,

Yun,

Wang

et al. 2024

AJST

View full text Add to dashboard Cite

show abstract

“…As a crucial part of driver assistance and automated driving systems, the radar sensor is considered to adapt to bad weather and poor lighting conditions 16 and it is mainly applied for the automatic braking system and the intelligent cruise control system 17 . Wei et al 18 developed a method for front vehicle detection based on machine learning. Jing et al 19 described an obstacle detection method based on the Doppler feature.…”

Section: Introductionmentioning

confidence: 99%

Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test

Lv,

Yuan,

Bai

2024

Sci Rep

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With the rapid development of intelligent and connected vehicles, the experimental road test for the advanced driving assistance system (ADAS) is dramatically increasing around the world. Considering its high cost and hazardous situations, simulation test based on a dummy model is becoming a promising way for ADAS road test practice to reduce the experiment expanses. This study proposed a methodology for the evaluation of the performance of human and dummies with distinct designed materials based on the data extracted from the Doppler effect of millimeter-wave radar. Echo data of 8 different angles from 0 to 360 degrees, with the an interval of 45 degrees, at the same distance between the test object and the signal source is collected. Meanwhile, the echo energy is collected for correlation modeling and analysis among groups. By evaluating the performance of humans and dummies via statistical analysis, a close correlation was found which results verified the substitutability of the dummy for the ADAS experiment test. The correlation coefficient between human and dummies ranges from 0.75 to 0.93. The support vector machine (SVM) model was developed and fitted to predict the echo energy in diverse environments. The mean average error (MAE) is 5.42–11.42 in the training and testing datasets while root mean square error (RMSE) is 0.43–0.90. The methods developed in the study can simulate the real ADAS road test environment and support future experimental research.

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“…As a crucial part of the driver assistance and the automated driving systems, the radar sensor is considered to adapt to bad weather and poor lighting conditions [16] and it is main applied for the automatic braking system and the intelligent cruise control system [17]. Wei et al [18] developed a method for front vehicle detection based on machine learning. Jing et al [19] described an obstacle detection method based on the Doppler feature.…”

Section: Introductionmentioning

confidence: 99%

Using Millimeter-Wave Radar to Evaluate the Performance of Dummy Models for Advanced Driving Assistance Systems Test

Lv,

Yuan,

Bai

2023

Preprint

View full text Add to dashboard Cite

With the rapid development of intelligent and connected vehicles, the experimental road test for Advanced Driving Assistance System (ADAS) is dramatically increasing around the world. Considering its high cost and hazardous situations, simulation test based on dummy model is becoming a promising way for ADAS road test practice to reduce the experiment expanses. This study proposed a methodology for the evaluation of the performance of human and dummies with distinct designed materials based on the data extracted from the Doppler effect of millimeter-wave radar. Echo data of 8 different angles from 0 to 360 degrees, with the interval of 45 degrees, at the same distance between the test object and the signal source are collected. Meanwhile, the echo energy is collected for correlation modeling and analysis among groups. By evaluating the performance of human and dummies via statistical analysis, a close correlation was found which results verified the substitutability of the dummy for ADAS experiment test. The correlation coefficient between human and dummies ranges from 0.75 to 0.93. The support vector machine (SVM) model was developed and fitted to predict the echo energy in diverse environments. The Mean Average Error is 5.42 to 11.42 in the training and testing datasets while Root Mean Square Error is 0.43 to 0.90. The methods developed in the study can simulate the real ADAS road test environment and support future experimental research.

show abstract

Area-Based CFAR Target Detection for Automotive Millimeter-Wave Radar

Cited by 11 publications

References 56 publications

Design of a Multidimensional Intelligent Safety Driving Monitoring System Based on Machine Vision

Design of a Multidimensional Intelligent Safety Driving Monitoring System Based on Machine Vision

Using millimeter-wave radar to evaluate the performance of dummy models for advanced driving assistance systems test

Using Millimeter-Wave Radar to Evaluate the Performance of Dummy Models for Advanced Driving Assistance Systems Test

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