Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 51-58 |
| Seitenumfang | 8 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 4 |
| Ausgabenummer | 1W1 |
| Publikationsstatus | Veröffentlicht - 30 Mai 2017 |
| Veranstaltung | ISPRS Hannover Workshop 2017: HRIGI - High-Resolution Earth Imaging for Geospatial Information, CMRT - City Models, Roads and Traffic, ISA - Image Sequence Analysis, EuroCOW - European Calibration and Orientation Workshop - Hannover, Hannover, Deutschland Dauer: 6 Juni 2017 → 9 Juni 2017 |
Abstract
We present a method for the generation of lane-accurate road network maps from vehicle trajectory data (GPS or better). Our approach therefore allows for exploiting today's connected vehicle fleets for the generation of HAD maps. The presented algorithm is based on elementary building blocks which guarantees useful lane models and uses a Reversible Jump Markov chain Monte Carlo method to explore the models parameters in order to reconstruct the one most likely emitting the input data. The approach is applied to a challenging urban real-world scenario of different trajectory accuracy levels and is evaluated against a LIDAR-based ground truth map.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Physik und Astronomie (insg.)
- Instrumentierung
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in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 4, Nr. 1W1, 30.05.2017, S. 51-58.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - EXTRACTING LANE GEOMETRY and TOPOLOGY INFORMATION from VEHICLE FLEET TRAJECTORIES in COMPLEX URBAN SCENARIOS USING A REVERSIBLE JUMP MCMC METHOD
AU - Roeth, O.
AU - Zaum, D.
AU - Brenner, Claus
N1 - Publisher Copyright: © 2017 Copernicus GmbH. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2017/5/30
Y1 - 2017/5/30
N2 - Highly automated driving (HAD) requires maps not only of high spatial precision but also of yet unprecedented actuality. Traditionally small highly specialized fleets of measurement vehicles are used to generate such maps. Nevertheless, for achieving city-wide or even nation-wide coverage, automated map update mechanisms based on very large vehicle fleet data gain importance since highly frequent measurements are only to be obtained using such an approach. Furthermore, the processing of imprecise mass data in contrast to few dedicated highly accurate measurements calls for a high degree of automation.We present a method for the generation of lane-accurate road network maps from vehicle trajectory data (GPS or better). Our approach therefore allows for exploiting today's connected vehicle fleets for the generation of HAD maps. The presented algorithm is based on elementary building blocks which guarantees useful lane models and uses a Reversible Jump Markov chain Monte Carlo method to explore the models parameters in order to reconstruct the one most likely emitting the input data. The approach is applied to a challenging urban real-world scenario of different trajectory accuracy levels and is evaluated against a LIDAR-based ground truth map.
AB - Highly automated driving (HAD) requires maps not only of high spatial precision but also of yet unprecedented actuality. Traditionally small highly specialized fleets of measurement vehicles are used to generate such maps. Nevertheless, for achieving city-wide or even nation-wide coverage, automated map update mechanisms based on very large vehicle fleet data gain importance since highly frequent measurements are only to be obtained using such an approach. Furthermore, the processing of imprecise mass data in contrast to few dedicated highly accurate measurements calls for a high degree of automation.We present a method for the generation of lane-accurate road network maps from vehicle trajectory data (GPS or better). Our approach therefore allows for exploiting today's connected vehicle fleets for the generation of HAD maps. The presented algorithm is based on elementary building blocks which guarantees useful lane models and uses a Reversible Jump Markov chain Monte Carlo method to explore the models parameters in order to reconstruct the one most likely emitting the input data. The approach is applied to a challenging urban real-world scenario of different trajectory accuracy levels and is evaluated against a LIDAR-based ground truth map.
KW - DGPS
KW - GPS
KW - IMU
KW - Lane Accurate Map Construction
KW - Reversible-Jump Markov chain Monte Carlo
KW - Road Network
KW - Trajectories
UR - http://www.scopus.com/inward/record.url?scp=85044539676&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-IV-1-W1-51-2017
DO - 10.5194/isprs-annals-IV-1-W1-51-2017
M3 - Conference article
AN - SCOPUS:85044539676
VL - 4
SP - 51
EP - 58
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 1W1
T2 - ISPRS Hannover Workshop 2017 on High-Resolution Earth Imaging for Geospatial Information, HRIGI 2017, City Models, Roads and Traffic , CMRT 2017, Image Sequence Analysis, ISA 2017, European Calibration and Orientation Workshop, EuroCOW 2017
Y2 - 6 June 2017 through 9 June 2017
ER -