Magnetic Sensor-Based Multi-Vehicle Data Association

Feng, Y; Andrew Zhang, J; Cheng, B; He, X; Chen, J

Magnetic Sensor-Based Multi-Vehicle Data Association

Feng, Y

Andrew Zhang, J Cheng, B He, X

Chen, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Sensors Journal, 2021, 21, (21), pp. 24709-24719
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Feng, Y https://orcid.org/0000-0002-3605-6829
dc.contributor.author	Andrew Zhang, J
dc.contributor.author	Cheng, B
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X
dc.contributor.author	Chen, J
dc.date.accessioned	2022-01-27T23:50:48Z
dc.date.available	2022-01-27T23:50:48Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Sensors Journal, 2021, 21, (21), pp. 24709-24719
dc.identifier.issn	1530-437X
dc.identifier.issn	1558-1748
dc.identifier.uri	http://hdl.handle.net/10453/153686
dc.description.abstract	Sensors have been playing an increasingly important role in smart cities. Using small roadside magnetic sensors provides a cost-efficient method for monitoring vehicle traffic. However, there are significant challenges associated with vehicle data misalignment due to the timing-offsets between sensors and missed or increased data because of vehicle lane-changing. In this paper, we propose a novel traffic information acquisition and vehicle state estimation scheme using multiple road magnetic sensors. To efficiently solve the multi-sensor registration problem in the presence of timing-offset, we develop a linear discrimination analysis method to achieve vehicle separation and classification. To handle the situation of lane-changing, we propose a data smoothing technique based on a multi-hypotheses tracker that exploits vehicle correlation. The road density effect on the probability of correct data association is investigated, with numerical and experimental results provided. The results show that our proposed scheme can effectively detect vehicles with a 95.5% accuracy rate. It also outperforms some other speed sensing methods in terms of the vehicle speed estimation accuracy.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Journal
dc.relation.isbasedon	10.1109/JSEN.2021.3112161
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Analytical Chemistry
dc.title	Magnetic Sensor-Based Multi-Vehicle Data Association
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-09-13T00:00:00+1000Z
dc.date.updated	2022-01-27T23:50:45Z
pubs.issue	21
pubs.publication-status	Published
pubs.volume	21
utslib.citation.issue	21

Abstract:

Sensors have been playing an increasingly important role in smart cities. Using small roadside magnetic sensors provides a cost-efficient method for monitoring vehicle traffic. However, there are significant challenges associated with vehicle data misalignment due to the timing-offsets between sensors and missed or increased data because of vehicle lane-changing. In this paper, we propose a novel traffic information acquisition and vehicle state estimation scheme using multiple road magnetic sensors. To efficiently solve the multi-sensor registration problem in the presence of timing-offset, we develop a linear discrimination analysis method to achieve vehicle separation and classification. To handle the situation of lane-changing, we propose a data smoothing technique based on a multi-hypotheses tracker that exploits vehicle correlation. The road density effect on the probability of correct data association is investigated, with numerical and experimental results provided. The results show that our proposed scheme can effectively detect vehicles with a 95.5% accuracy rate. It also outperforms some other speed sensing methods in terms of the vehicle speed estimation accuracy.

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http://hdl.handle.net/10453/153686