Evidence map›Paper›PMID 31795212›Full record

ArticleSensors (Basel, Switzerland)2019

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

Vytautas Markevicius, Dangirutis Navikas, Adam Idzkowski, Donatas Miklusis, Darius Andriukaitis, Algimantas Valinevicius, Mindaugas Zilys, Mindaugas Cepenas, Wojciech Walendziuk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Vytautas MarkeviciusDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Dangirutis NavikasDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Adam IdzkowskiDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Donatas MiklusisDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Darius AndriukaitisDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Algimantas ValineviciusDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Mindaugas ZilysDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Mindaugas CepenasDepartment of Electronics Engineering, Kaunas University of Technology, Studentu St. 50-439, LT-51368 Kaunas, Lithuania.
Wojciech WalendziukFaculty of Electrical Engineering, Bialystok University of Technology; Wiejska St. 45D, PL-15351 Bialystok, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AMR sensorscar length estimationmagnetic fieldMean Absolute Error.piezoelectric PVDF sensorssignal differentiationvehicle speed detection

Identifiers

PMID31795212
PMCPMC6929070

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.