Vehicle Speed and Length Estimation Using Data from Two Anisotropic Magneto-Resistive (AMR) Sensors

Methods for estimating a car’s length are presented in this paper, as well as the results achieved by using a self-designed system equipped with two anisotropic magneto-resistive (AMR) sensors, which were placed on a road lane. The purpose of the research was to compare the lengths of mid-size cars, i.e., family cars (hatchbacks), saloons (sedans), station wagons and SUVs. Four methods were used in the research: a simple threshold based method, a threshold method based on moving average and standard deviation, a two-extreme-peak detection method and a method based on the amplitude and time normalization using linear extrapolation (or interpolation). The results were achieved by analyzing changes in the magnitude and in the absolute z-component of the magnetic field as well. The tests, which were performed in four different Earth directions, show differences in the values of estimated lengths. The magnitude-based results in the case when cars drove from the South to the North direction were even up to 1.2 m higher than the other results achieved using the threshold methods. Smaller differences in lengths were observed when the distances were measured between two extreme peaks in the car magnetic signatures. The results were summarized in tables and the errors of estimated lengths were presented. The maximal errors, related to real lengths, were up to 22%.

[1]  Rajesh Rajamani,et al.  Portable Roadside Sensors for Vehicle Counting, Classification, and Speed Measurement , 2014, IEEE Transactions on Intelligent Transportation Systems.

[2]  V. Markevicius,et al.  Vehicle Influence on the Earth’s Magnetic Field Changes , 2014 .

[3]  Benjamin Coifman,et al.  Speed Estimation and Length Based Vehicle Classification from Freeway Single Loop Detectors , 2009 .

[4]  Ilija Jolevski,et al.  Smart vehicle sensing and classification node with energy aware vehicle classification algorithm , 2011, Proceedings of the ITI 2011, 33rd International Conference on Information Technology Interfaces.

[5]  Fengqi Yu,et al.  A Cross-Correlation Technique for Vehicle Detections in Wireless Magnetic Sensor Network , 2016, IEEE Sensors Journal.

[6]  Pravin Varaiya,et al.  Wireless magnetic sensors for traffic surveillance , 2008 .

[7]  Antonio Moschitta,et al.  A simple magnetic signature vehicles detection and classification system for Smart Cities , 2016, 2016 IEEE International Symposium on Systems Engineering (ISSE).

[8]  Pavel Ripka,et al.  Advances in Magnetic Field Sensors , 2010, IEEE Sensors Journal.

[9]  Ignacio Parra,et al.  Two-camera based accurate vehicle speed measurement using average speed at a fixed point , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[10]  Saowaluck Kaewkamnerd,et al.  Automatic vehicle classification using wireless magnetic sensor , 2009, 2009 IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications.

[11]  Yoichiro Iwasaki A Method of Real-time Moving Vehicle Detection for Bad Environments Using Infrared Thermal Images , 2008 .

[12]  Algimantas Valinevicius,et al.  Dynamic Vehicle Detection via the Use of Magnetic Field Sensors , 2016, Sensors.

[13]  L. Roselli,et al.  Measurements of length and velocity of vehicles with a low cost sensor radar Doppler operating at 24GHz , 2007, 2007 2nd International Workshop on Advances in Sensors and Interface.

[14]  Vladimir Dyo,et al.  Wireless Magnetic Sensor Network for Road Traffic Monitoring and Vehicle Classification , 2016 .

[15]  David Vala,et al.  Using spread spectrum for AMR magnetic sensor , 2016, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).