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Graph Laplacian Diffusion Localization of Connected and Automated Vehicles

descriptionPublicationkeyboard_double_arrow_right Preprint , Article 01 Aug 2022Institute of Electrical and Electronics Engineers (IEEE)IEEE Transactions on Intelligent Transportation Systems, volume 23, pages 12,176-12,190 (issn: 1524-9050, eissn: 1558-0016,

Authors: Nikos Piperigkos; Aris S. Lalos; Kostas Berberidis;

doi: 10.1109/tits.2021.3110650 , 10.48550/arxiv.2108.10678 , 10.5281/zenodo.6381510 , 10.5281/zenodo.6381511

Graph Laplacian Diffusion Localization of Connected and Automated Vehicles

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Abstract

In this paper, we design distributed multi-modal localization approaches for Connected and Automated vehicles. We utilize information diffusion on graphs formed by moving vehicles, based on Adapt-then-Combine strategies combined with the Least-Mean-Squares and the Conjugate Gradient algorithms. We treat the vehicular network as an undirected graph, where vehicles communicate with each other by means of Vehicle-to- Vehicle communication protocols. Connected vehicles perform cooperative fusion of different measurement modalities, including location and range measurements, in order to estimate both their positions and the positions of all other networked vehicles, by interacting only with their local neighborhood. The trajectories of vehicles were generated either by a well-known kinematic model, or by using the CARLA autonomous driving simulator. The various proposed distributed and diffusion localization schemes significantly reduce the GPS error and do not only converge to the global solution, but they even outperformed it. Extensive simulation studies highlight the benefits of the various approaches, outperforming the accuracy of the state of the art approaches. The impact of the network connections and the network latency are also investigated.

Related Organizations

University of Patras
Greece

Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science Real-time computing Driving simulator Kinematics Error analysis for the Global Positioning System Range (mathematics) Conjugate gradient method State (computer science) Laplacian matrix Communications protocol

arXiv: Computer Science::Robotics

Keywords

Signal Processing (eess.SP), Mechanical Engineering, Computer Science Applications, Automotive Engineering, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing

33 references, page 1 of 4

[1] S. Kuutti, S. Fallah, K. Katsaros, M. Dianati, F. Mccullough, and A. Mouzakitis, “A survey of the state-of-the-art localization techniques and their potentials for autonomous vehicle applications,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 829-846, 2018. [OpenAIRE]

[2] R. T. Ioannides, T. Pany, and G. Gibbons, “Known vulnerabilities of global navigation satellite systems, status, and potential mitigation techniques,” Proceedings of the IEEE, vol. 104, no. 6, pp. 1174-1194, 2016.

[3] N. Alam and A. G. Dempster, “Cooperative positioning for vehicular networks: Facts and future,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1708-1717, 2013.

[4] J. B. P. Neto, L. C. Gomes, F. M. Ortiz, T. T. Almeida, M. E. M. Campista, L. H. M. Costa, and N. Mitton, “An accurate cooperative positioning system for vehicular safety applications,” Computers & Electrical Engineering, vol. 83, p. 106591, 2020.

[5] U. Montanaro, S. Dixit, S. Fallah, M. Dianati, A. Stevens, D. Oxtoby, and A. Mouzakitis, “Towards connected autonomous driving: review of use-cases,” Vehicle System Dynamics, vol. 57, no. 6, pp. 779-814, 2018.

[6] M. Elazab, A. Noureldin, and H. S. Hassanein, “Integrated cooperative localization for vehicular networks with partial GPS access in urban canyons,” Vehicular Communications, vol. 9, pp. 242-253, 2017. [OpenAIRE]

[7] G. Soatti, M. Nicoli, N. Garcia, B. Denis, R. Raulefs, and H. Wymeersch, “Implicit cooperative positioning in vehicular networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 12, pp. 3964- 3980, 2018.

[8] M. Rohani, D. Gingras, V. Vigneron, and D. Gruyer, “A new decentralized bayesian approach for cooperative vehicle localization based on fusion of GPS and VANET based inter-vehicle distance measurement,” IEEE Intelligent Transportation Systems Magazine, vol. 7, no. 2, pp. 85-95, 2015.

[9] F. Cattivelli and A. Sayed, “Diffusion LMS strategies for distributed estimation,” IEEE Transactions on Signal Processing, vol. 58, no. 3, pp. 1035-1048, 2010.

[10] R. Nassif, S. Vlaski, C. Richard, J. Chen, and A. H. Sayed, “Multitask learning over graphs: An approach for distributed, streaming machine learning,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 14-25, 2020. [OpenAIRE]

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	2
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visibility	views	40
download	downloads	59

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	2
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average

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