Research on Geomagnetic Waveform Detection and Vehicle Identification Technology

Guan, Yaqi; Li, Wenqiang; Tan, Jun; Zhu, Yiming; Ding, Xuefeng; Xiong, Yunke; Hou, Qun

doi:10.1109/iaeac50856.2021.9390690

Cited by 3 publications

(3 citation statements)

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“…To address this issue, previous research detected the presence of automobiles by measuring the geomagnetic field fluctuation caused by the automobiles [56,57]. In these studies, the intensity of the geomagnetic field fluctuating near the automobile was observed to be around 8 µG for a distance between the magnetic sensor and the automobile of 50 m and around 1 µG for a distance of 100 m.…”

Section: Discussionmentioning

confidence: 99%

Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments

Takebayashi

Miyagusuku

Ozaki

2021

Sensors

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Localization is fundamental to enable the use of autonomous mobile robots. In this work, we use magnetic-based localization. As Earth’s geomagnetic field is stable in time and is not affected by nonmagnetic materials, such as a large number of people in the robot’s surroundings, magnetic-based localization is ideal for service robotics in supermarkets, hotels, etc. A common approach for magnetic-based localization is to first create a magnetic map of the environment where the robot will be deployed. For this, magnetic samples acquired a priori are used. To generate this map, the collected data is interpolated by training a Gaussian Process Regression model. Gaussian processes are nonparametric, data-drive models, where the most important design choice is the selection of an adequate kernel function. These models are flexible and generate mean predictions as well as the confidence of those predictions, making them ideal for their use in probabilistic approaches. However, their computational and memory cost scales poorly when large datasets are used for training, making their use in large-scale environments challenging. The purpose of this study is to: (i) enable magnetic-based localization on large-scale environments by using a sparse representation of Gaussian processes, (ii) test the effect of several kernel functions on robot localization, and (iii) evaluate the accuracy of the approach experimentally on different large-scale environments.

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Section: Discussionmentioning

confidence: 99%

Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments

Takebayashi

Miyagusuku

Ozaki

2021

Sensors

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“…In the recent years, wireless sensors have received significant attention in automated systems [ 19 , 20 , 21 ]. The authors of [ 19 ] proposed a sensing node consisting of two magnetic field sensors on the roadside and one on the road.…”

Section: Relate Workmentioning

confidence: 99%

“…Magnetic readings of the z-axis from the multiple sensors were used for vehicle type estimation and classification. In [ 20 ], vehicle classification is accomplished only from one node sensor. The authors shared future plans to collect a large dataset and arrange data into four main groups.…”

Section: Relate Workmentioning

confidence: 99%

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

Miklusis

Markevičius

Navikas

et al. 2021

Sensors

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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.

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Research on Pavement Traffic Load State Perception Based on the Piezoelectric Effect

Jiang

Sha

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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Research on Geomagnetic Waveform Detection and Vehicle Identification Technology

Cited by 3 publications

References 0 publications

Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments

Development of Magnetic-Based Navigation by Constructing Maps Using Machine Learning for Autonomous Mobile Robots in Real Environments

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

Research on Pavement Traffic Load State Perception Based on the Piezoelectric Effect

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