Research of distorted vehicle magnetic signatures recognitions, for length estimation in real traffic conditions

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.Lietuvos Mokslo Taryba | Ref. S-MIP-21-3

Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array

The increasing presence of vehicles on roads necessitates intelligent traffic management solutions in areas where video cameras cannot be utilized. Currently, there are limited choices for depersonalized vehicle reidentification systems. This paper introduces a system that later will be used for vehicle reidentification. The system uses anisotropic magnetoresistive sensors and is based on the hypothesis that each vehicle leaves unique magnetic signatures which can be used for comparison and matching. Vehicle location on the road perpendicular to sensor array detection methodology is presented in this work. An array of magnetic sensors is installed in asphalt across the vehicle’s driving direction. The system continuously measures Earth’s natural magnetic field and detects distortions when vehicles pass an array of a sensors and then logs magnetic signatures. Useful parameters from raw sensor axes are calculated– modules and derivatives. Applying signal-to-noise ratio calculation for module derivatives between ambient noise and signal gives important features for neural network input. Different types of neural network architectures and output result interpretation techniques are investigated. Further, after evaluating network output it is possible to label sensor nodes that are directly beneath the vehicle. Experiment results show that implemented algorithm is highly sufficient for valid sensors under the vehicle selection. Correct sensor selection is important for further re-identification algorithms