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Journal Article

Citation

Markevicius V, Navikas D, Idzkowski A, Miklusis D, Andriukaitis D, Valinevicius A, Zilys M, Cepenas M, Walendziuk W. Sensors (Basel) 2019; 19(23): s19235234.

Affiliation

Faculty of Electrical Engineering, Bialystok University of Technology; Wiejska St. 45D, PL-15351 Bialystok, Poland.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19235234

PMID

31795212

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.


Language: en

Keywords

AMR sensors; Mean Absolute Error.; car length estimation; magnetic field; piezoelectric PVDF sensors; signal differentiation; vehicle speed detection

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