Research of Distorted Vehicle Magnetic Signatures Recognitions, for Length Estimation in Real Traffic Conditions

Abstract

Reliable cost-effective traffic monitoring stations are a key component of intelligent transportation systems (ITS). While modern surveillance camera systems provide a high amount of data, due to high installation price or invasion of drivers’ personal privacy, they are not the right technology. Therefore, in this paper we introduce a traffic flow parameterization system, using a built-in pavement sensing hub of a pair of AMR (anisotropic magneto resistance) magnetic field and MEMS (micro-electromechanical system) accelerometer sensors. In comparison with inductive loops, AMR magnetic sensors are significantly cheaper, have lower installation price and cause less intrusion to the road. The developed system uses magnetic signature to estimate vehicle speed and length. While speed is obtained from the cross-correlation method, a novel vehicle length estimation algorithm based on characterization of the derivative of magnetic signature is presented. The influence of signature filtering, derivative step and threshold parameter on estimated length is investigated. Further, accelerometer sensors are employed to detect when the wheel of vehicle passes directly over the sensor, which cause distorted magnetic signatures. Results show that even distorted signatures can be used for speed estimation, but it must be treated with a more robust method. The database during the real-word traffic and hazard environmental condition was collected over a 0.5-year period and used for method validation.

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

“…On the other hand, to have a more compact sensor hub, a cross-correlation algorithm is preferred. In our previous studies [ 17 , 29 ], we demonstrated a speed estimation system with 0.3 m spaced sensors and MAPE below 3%. Even though for most of the applications this error margin is more than enough, it was noted that under certain conditions the estimated velocity result is highly erroneous.…”

“…The utilized hardware is comprehensively described in our previous works [ 17 , 29 ]. As shown in Figure 1 , the system is composed of two laterally spaced anisotropic resistance magnetometer (AMR) sensors.…”

“…Magnetic field sensors provide feature-rich signals of passing vehicles, but those signals are dependent on multiple factors (e.g., vehicle height, acceleration, and driving trajectory). It has been shown in previously conducted research [ 27 , 28 , 29 , 30 ] that cross-correlation is the most accurate method for vehicle speed estimation from a pair of magnetic signatures. In Figure 2 b, multiple magnetic signatures of the same vehicle are shown versus the length.…”

The practical design of a vehicle magnetic signature generator

“…The vehicle presence is detected based on the magnitude of the magnetic field, and the duration of magnetic field distortion (proximity of the vehicle to the sensor) (Vanc xin and Erdem, 2018). The length estimation of a vehicle in natural traffic conditions is performed by a magnetic sensor and accelerometer (Miklusis et al, 2021).…”

Real-Time Vehicle Classification System Using a Single Magnetometer

“…Accelerometers mounted on the pavement surface can measure vibrations caused by vehicles passing near the sensor. The use of accelerometers alone was also reported in the literature [ 13 ], but they were mainly utilized together with magnetic sensors [ 14 , 15 , 16 , 17 ]. In these systems, accelerometer data are applied to identify axle locations using peak detection algorithms, while the magnetometers are used to determine vehicle arrival, departure, and speed.…”