Next Article in Journal
Multiple-Antenna Emitters Identification Based on a Memoryless Power Amplifier Model
Next Article in Special Issue
Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data
Previous Article in Journal
Vessel Detection and Tracking Method Based on Video Surveillance
Previous Article in Special Issue
Estimating Driving Fatigue at a Plateau Area with Frequent and Rapid Altitude Change
Open AccessArticle

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

1
Department of Electronics Engineering, Kaunas University of Technology, Studentu St. 50–439, LT-51368 Kaunas, Lithuania
2
Faculty of Electrical Engineering, Bialystok University of Technology; Wiejska St. 45D, PL-15351 Bialystok, Poland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5234; https://doi.org/10.3390/s19235234
Received: 5 November 2019 / Revised: 24 November 2019 / Accepted: 27 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Advance in Sensors and Sensing Systems for Driving and Transportation)
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. View Full-Text
Keywords: magnetic field; AMR sensors; piezoelectric PVDF sensors; vehicle speed detection; car length estimation; signal differentiation; Mean Absolute Error magnetic field; AMR sensors; piezoelectric PVDF sensors; vehicle speed detection; car length estimation; signal differentiation; Mean Absolute Error
Show Figures

Figure 1

MDPI and ACS Style

Markevicius, V.; Navikas, D.; Idzkowski, A.; Miklusis, D.; Andriukaitis, D.; Valinevicius, A.; Zilys, M.; Cepenas, M.; Walendziuk, W. Vehicle Speed and Length Estimation Errors Using the Intelligent Transportation System with a Set of Anisotropic Magneto-Resistive (AMR) Sensors. Sensors 2019, 19, 5234.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop