A single node vehicle detection system using an adaptive signal adjustment technique

Kwon, Young-Jin; Kim, Dohyun; Choi, Kyoung-Ho

doi:10.1109/wpmc.2017.8301836

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…Kwon et al [ 26 ] used only a 20 cm spaced sensor pair for speed estimation. The time delay was calculated by modified cross-correlation function and adaptive signal rescaling.…”

Section: Related Work: Comparison Of Speed Estimation Resultsmentioning

confidence: 99%

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Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors

Miklusis

Markevičius

Navikas

et al. 2022

Sensors

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Magnetic field sensors installed in the road infrastructure can be used for autonomous traffic flow parametrization. Although the main goal of such a measuring system is the recognition of the class of vehicle and classification, velocity is the essential parameter for further calculation and it must be estimated with high reliability. In-field test campaigns, during actual traffic conditions, showed that commonly accepted velocity estimation methods occasionally produce highly erroneous results. For anomaly detection, we propose a criterion and two different correction algorithms. Non-linear signal rescaling and time-based segmentation algorithms are presented and compared for faulty result mitigation. The first one consists of suppressing the highly distorted signal peaks and looking for the best match with cross-correlation. The second approach relies on signals segmentation according to the feature points and multiple cross-correlation comparisons. The proposed two algorithms are evaluated with a dataset of over 300 magnetic signatures of a vehicle from unconstraint traffic conditions. Results show that the proposed criteria highlight all greatly faulty results and that the correction algorithms reduce the maximum error by twofold, but due to the increased mean error, mitigation technics shall be used explicitly with distorted signals.

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“…Kwon et al [ 26 ] used only a 20 cm spaced sensor pair for speed estimation. The time delay was calculated by modified cross-correlation function and adaptive signal rescaling.…”

Section: Related Work: Comparison Of Speed Estimation Resultsmentioning

confidence: 99%

“…Inspired by modified cross-correlation algorithms from [ 24 , 26 ] it was proposed to utilize magnetic signature segmentation in order to isolate distorted features. Signal segmentation is performed on the first order derivative of the signature since it crosses the zero level multiple times.…”

Section: Methodsmentioning

confidence: 99%

Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors

Miklusis

Markevičius

Navikas

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…They depend on the distance between sensors, speed of vehicle and the filtering signals method used. As a comparison, the errors for variously constructed systems with magnetic sensors placed on the road lane at a distance D = 6 m are RMSE = 5.1 km/h, MAPE = 2.65% ( MAPE - Mean Absolute Percentage Error) [27], and at a distance of 20 cm MAPE = 4.16% or 2.10% [28]. With regards to the system with induction loops, in publication [29], accuracy measures were given for four categories of vehicles: RMSE —from 2.5 km/h to 8.6 km/h, MAPE —from 1.8% to 9%.…”

Section: Discussionmentioning

confidence: 99%

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

Markevičius

Navikas

Idźkowski

et al. 2019

Sensors

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Seeking an effective method for estimating the speed and length of a car is still a challenge for engineers and scientists who work on intelligent transportation systems. This paper focuses on a self-developed system equipped with four anisotropic magneto-resistive (AMR) sensors which are placed on a road lane. The piezoelectric polyvinylidene fluoride (PVDF) sensors are also mounted and used as a reference device. The methods applied in the research are well-known: the fixed threshold-based method and the adaptive two-extreme-peak detection method. However, the improved accuracy of estimating the length by using one of the methods, which is based on computing the difference quotient of a time-discrete signal (representing the changes in the magnitude of the magnetic field of the Earth), is observed. The obtained results, i.e., the speed and length of a vehicle, are presented for various values of the increment Δn used in numerical differentiation of magnetic field magnitude data. The results were achieved in real traffic conditions after analyzing a data set M = 290 of vehicle signatures. The accuracy was evaluated by calculating MAE (Mean Absolute Error), RMSE (Root Mean Squared Error) for different classes of vehicles. The MAE is within the range of 0.52 m–1.18 m when using the appropriate calibration factor. The results are dependent on the distance between sensors, the speed of vehicle and the signal processing method applied.

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“…Due to the systematic error caused by the radar device, the two boundaries present a certain confidence level for describing the effective angle of the target. In this paper, we define the effective confidence level as the proportion of max with the boundary value by Equation (5) and is also shown in Figure 5: It is evident from Figure 4 that the angular values decline to the minimum value as the target moves farther from the radar device. Considering this, the systematic error makes an everincreasing effect on the accuracy of the angle value of the target as shown in Figure 5.…”

Section: Angle Threshold Analysismentioning

confidence: 99%

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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A single node vehicle detection system using an adaptive signal adjustment technique

Cited by 7 publications

References 9 publications

Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors

Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors

Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors

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

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