Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2016 IEEE Intelligent Vehicles Symposium, IV 2016 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 194-201 |
| Seitenumfang | 8 |
| ISBN (elektronisch) | 9781509018215 |
| Publikationsstatus | Veröffentlicht - 5 Aug. 2016 |
| Veranstaltung | 2016 IEEE Intelligent Vehicles Symposium, IV 2016 - Gotenburg, Schweden Dauer: 19 Juni 2016 → 22 Juni 2016 |
Publikationsreihe
| Name | IEEE Intelligent Vehicles Symposium, Proceedings |
|---|---|
| Band | 2016-August |
Abstract
Being regarded as essential for advanced driver assistance systems and autonomous driving, the research field of map generation from crowd-sourced vehicle information gained attention in the last decade. This paper introduces a novel approach for the derivation of street accurate road networks from such data. The method is applied to a real-world dataset of different accuracy level and is evaluated against a ground truth map. Furthermore, the results are compared to results of two state of the art algorithms.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Angewandte Informatik
- Ingenieurwesen (insg.)
- Fahrzeugbau
- Mathematik (insg.)
- Modellierung und Simulation
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- BibTex
- RIS
2016 IEEE Intelligent Vehicles Symposium, IV 2016. Institute of Electrical and Electronics Engineers Inc., 2016. S. 194-201 7535385 (IEEE Intelligent Vehicles Symposium, Proceedings; Band 2016-August).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Road network reconstruction using reversible jump MCMC simulated annealing based on vehicle trajectories from fleet measurements
AU - Roeth, Oliver
AU - Zaum, Daniel
AU - Brenner, Claus
PY - 2016/8/5
Y1 - 2016/8/5
N2 - Being regarded as essential for advanced driver assistance systems and autonomous driving, the research field of map generation from crowd-sourced vehicle information gained attention in the last decade. This paper introduces a novel approach for the derivation of street accurate road networks from such data. The method is applied to a real-world dataset of different accuracy level and is evaluated against a ground truth map. Furthermore, the results are compared to results of two state of the art algorithms.
AB - Being regarded as essential for advanced driver assistance systems and autonomous driving, the research field of map generation from crowd-sourced vehicle information gained attention in the last decade. This paper introduces a novel approach for the derivation of street accurate road networks from such data. The method is applied to a real-world dataset of different accuracy level and is evaluated against a ground truth map. Furthermore, the results are compared to results of two state of the art algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84983371909&partnerID=8YFLogxK
U2 - 10.1109/IVS.2016.7535385
DO - 10.1109/IVS.2016.7535385
M3 - Conference contribution
AN - SCOPUS:84983371909
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 194
EP - 201
BT - 2016 IEEE Intelligent Vehicles Symposium, IV 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Intelligent Vehicles Symposium, IV 2016
Y2 - 19 June 2016 through 22 June 2016
ER -