Data Feature Analysis of Non-Scanning Multi Target Millimeter-Wave Radar in Traffic Flow Detection Applications

Liu, Haiqing; Li, Na; Guan, Deyong; Rai, Laxmisha

doi:10.3390/s18092756

Cited by 21 publications

(13 citation statements)

References 24 publications

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“…Referring to [26], when the radar device is installed above the road, the distance values of vehicles are distributed between

L_{\min}

and

L_{\max}

as described by Equations (1) and (2) in detail:

L_{\min} = \frac{H - h_{c}}{\cos (90 - θ - δ)}

L_{\max} = \frac{H}{\cos (90 - θ + δ)}

where

H

is the height of the radar device from the ground,

θ

is the pitch angle of the radar device,

δ

is the pitch angle of the radar signal, and

h_{c}

is the height of the vehicle.…”

Section: Abnormal Data Preprocessing Methods Based On Threshold Analysismentioning

confidence: 99%

“…Aiming at this new application scenario, a data analysis and processing method is proposed in this paper. Based on the data features acquired in [26], a two‐step data processing method is proposed, namely, threshold analysis for the first step and nearest neighbour analysis [27, 28] for the second step. In the first step, the rational range of distance, angle and speed are analysed, respectively.…”

Section: Introductionmentioning

confidence: 99%

“…The detailed parameters of the installation and the road section are introduced in Table 1. The radar used in this study is an improved version in terms of signal quality and radiation pattern, compared to the device used in [26]. Compared with the radar in the previous work, the planar microstrip array antenna contains two groups for transmission and four groups for receiving; it can detect a much farther target, and the angle value is more stable.…”

Section: Introduction To Experimental Scenario and Original Data Pres...mentioning

confidence: 99%

See 2 more Smart Citations

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

Liu

Teng

Rai

et al. 2021

IET Intelligent Trans Sys

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In a new application scenario where the millimetre‐wave radar is installed above the road for detecting traffic flow in downward looking direction, the original data of the radar includes all kinds of background noises and false targets. In order to acquire effective vehicle trajectories, a two‐step abnormal data processing method for millimetre‐wave radar in traffic flow detection application is proposed. In the first step, the rational range of distance, angle and speed are studied, and proper thresholds are presented for reducing the samples of which the single parameter is with the obvious abnormality. Moreover, the nearest neighbour analysis method is used to further extract vehicle trajectories based on the similarity and slope characteristics of each sample to its neighbours. Taking actual detected data as samples, the weighting coefficients, similarity threshold, average slope threshold and standard deviation threshold are calibrated for the proposed nearest neighbour analysis method. The two‐step processing method presents a higher performance in extracting effective trajectory samples, and the ratio of noise points is reduced to 4.1%, compared with 239.9% in the original data sample. The proposed method can provide an effective reference for further applications, such as driving behaviour analysis and traffic flow parameter identification.

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“…Referring to [26], when the radar device is installed above the road, the distance values of vehicles are distributed between

L_{\min}

and

L_{\max}

as described by Equations (1) and (2) in detail:

L_{\min} = \frac{H - h_{c}}{\cos (90 - θ - δ)}

L_{\max} = \frac{H}{\cos (90 - θ + δ)}

where

H

is the height of the radar device from the ground,

θ

is the pitch angle of the radar device,

δ

is the pitch angle of the radar signal, and

h_{c}

is the height of the vehicle.…”

Section: Abnormal Data Preprocessing Methods Based On Threshold Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introduction To Experimental Scenario and Original Data Pres...mentioning

confidence: 99%

See 1 more Smart Citation

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

Liu

Teng

Rai

et al. 2021

IET Intelligent Trans Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…And based on the detection and counting results, an estimation model is proposed to estimate traffic flow parameters of volume, speed and density. In [48], the authors collect the vehicle parameters by the radar data whose features are analyzed from the dimensions of single parameter sampling characteristics and multiparameter relationships. Further, the correlations of different traffic flow parameters are given using the grey correlation analysis method.…”

Section: Related Workmentioning

confidence: 99%

Traffic Flow Sensing Using Wireless Signals

2021

KSII TIIS

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“…In (27), ρ is the accommodation coefficient. Suppose that the minimum weight confidence level is ε when i = N C , the accommodation coefficient is calculated as follows:…”

Section: Real-time Queue Length Estimation Based On Markov Modelmentioning

confidence: 99%

A Real-Time Queue Length Estimation Method Based on Probe Vehicles in CV Environment

Hai-qing¹,

Liang²,

Rai³

et al. 2019

IEEE Access

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The real-time traffic status estimation in urban signalized intersections is highly valuable for modern traffic control and management. This paper presents a real-time queue length estimation method based on probe vehicles' data in the connected vehicle (CV) environment. The probe data are used to identify the stopping states of CVs. Based on this, a queue length time series referring to the stopping time and the positions of CVs is built for describing the queuing process at an intersection. Considering the statistical average traffic rate, queue length time series in historical cycles, and the stopping states for real-time CV arrival features in the current cycle, the critical queuing time is forecasted based on the linear fitting method and the real-time queue length is estimated based on the Markov model. The overall scheme is thoroughly tested and demonstrated in a realistic scenario at different penetration rates. Under different conditions, the stationarity of the queue length series is tested by the augmented Dickey-Fuller. Two Markov models based on the transition matrices of the current cycle and both the current and historical cycles are verified, respectively. The results demonstrate the high accuracy in the real-time queue length estimation, and the proposed method shows good performance in handling the randomness, especially when the CV penetration rate is low. INDEX TERMS Connected vehicle, Markov model, queue length, time series.

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Data Feature Analysis of Non-Scanning Multi Target Millimeter-Wave Radar in Traffic Flow Detection Applications

Cited by 21 publications

References 24 publications

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

A two‐step abnormal data analysis and processing method for millimetre‐wave radar in traffic flow detection applications

Traffic Flow Sensing Using Wireless Signals

A Real-Time Queue Length Estimation Method Based on Probe Vehicles in CV Environment

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