Boosting Performance of Map Matching Algorithms by Parallelization on Graphics Processors

Markus Auer; Hubert Rehborn; Sven Eric Molzahn; Klaus Bogenberger

doi:10.1109/IVS.2017.7995761

Boosting Performance of Map Matching Algorithms by Parallelization on Graphics Processors

Markus Auer, Hubert Rehborn, Sven Eric Molzahn, Klaus Bogenberger

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In this paper existing map matching algorithms are combined and modified such, that the resulting algorithm is suitable for the implementation on the graphics processing unit (GPU). The map matching algorithm implemented on GPU consists of a geometrical and topological processing step, which provides high accuracy with high efficiency at the same time. An important building block of the implementation is the parallelization of the RL-Tree search. An efficient implementation is achieved by high data parallelism and minimal divergence between execution blocks. The presented map matching algorithm performs better than available open source implementations.

Original language	English
Title of host publication	IV 2017 - 28th IEEE Intelligent Vehicles Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	462-467
Number of pages	6
ISBN (Electronic)	9781509048045
DOIs	https://doi.org/10.1109/IVS.2017.7995761
State	Published - 28 Jul 2017
Externally published	Yes
Event	28th IEEE Intelligent Vehicles Symposium, IV 2017 - Redondo Beach, United States Duration: 11 Jun 2017 → 14 Jun 2017

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference	28th IEEE Intelligent Vehicles Symposium, IV 2017
Country/Territory	United States
City	Redondo Beach
Period	11/06/17 → 14/06/17

Access to Document

10.1109/IVS.2017.7995761

Cite this

Auer, M., Rehborn, H., Molzahn, S. E., & Bogenberger, K. (2017). Boosting Performance of Map Matching Algorithms by Parallelization on Graphics Processors. In IV 2017 - 28th IEEE Intelligent Vehicles Symposium (pp. 462-467). Article 7995761 (IEEE Intelligent Vehicles Symposium, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2017.7995761

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title = "Boosting Performance of Map Matching Algorithms by Parallelization on Graphics Processors",

abstract = "In this paper existing map matching algorithms are combined and modified such, that the resulting algorithm is suitable for the implementation on the graphics processing unit (GPU). The map matching algorithm implemented on GPU consists of a geometrical and topological processing step, which provides high accuracy with high efficiency at the same time. An important building block of the implementation is the parallelization of the RL-Tree search. An efficient implementation is achieved by high data parallelism and minimal divergence between execution blocks. The presented map matching algorithm performs better than available open source implementations.",

author = "Markus Auer and Hubert Rehborn and Molzahn, {Sven Eric} and Klaus Bogenberger",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 28th IEEE Intelligent Vehicles Symposium, IV 2017 ; Conference date: 11-06-2017 Through 14-06-2017",

year = "2017",

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day = "28",

doi = "10.1109/IVS.2017.7995761",

language = "English",

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Auer, M, Rehborn, H, Molzahn, SE & Bogenberger, K 2017, Boosting Performance of Map Matching Algorithms by Parallelization on Graphics Processors. in IV 2017 - 28th IEEE Intelligent Vehicles Symposium., 7995761, IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 462-467, 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11/06/17. https://doi.org/10.1109/IVS.2017.7995761

Boosting Performance of Map Matching Algorithms by Parallelization on Graphics Processors. / Auer, Markus; Rehborn, Hubert; Molzahn, Sven Eric et al.
IV 2017 - 28th IEEE Intelligent Vehicles Symposium. Institute of Electrical and Electronics Engineers Inc., 2017. p. 462-467 7995761 (IEEE Intelligent Vehicles Symposium, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Auer, Markus

AU - Rehborn, Hubert

AU - Molzahn, Sven Eric

AU - Bogenberger, Klaus

PY - 2017/7/28

Y1 - 2017/7/28

N2 - In this paper existing map matching algorithms are combined and modified such, that the resulting algorithm is suitable for the implementation on the graphics processing unit (GPU). The map matching algorithm implemented on GPU consists of a geometrical and topological processing step, which provides high accuracy with high efficiency at the same time. An important building block of the implementation is the parallelization of the RL-Tree search. An efficient implementation is achieved by high data parallelism and minimal divergence between execution blocks. The presented map matching algorithm performs better than available open source implementations.

AB - In this paper existing map matching algorithms are combined and modified such, that the resulting algorithm is suitable for the implementation on the graphics processing unit (GPU). The map matching algorithm implemented on GPU consists of a geometrical and topological processing step, which provides high accuracy with high efficiency at the same time. An important building block of the implementation is the parallelization of the RL-Tree search. An efficient implementation is achieved by high data parallelism and minimal divergence between execution blocks. The presented map matching algorithm performs better than available open source implementations.

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M3 - Conference contribution

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T3 - IEEE Intelligent Vehicles Symposium, Proceedings

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BT - IV 2017 - 28th IEEE Intelligent Vehicles Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th IEEE Intelligent Vehicles Symposium, IV 2017

Y2 - 11 June 2017 through 14 June 2017

ER -

Boosting Performance of Map Matching Algorithms by Parallelization on Graphics Processors

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