Details
| Original language | English |
|---|---|
| Title of host publication | 2016 IEEE Intelligent Vehicles Symposium, IV 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 194-201 |
| Number of pages | 8 |
| ISBN (electronic) | 9781509018215 |
| Publication status | Published - 5 Aug 2016 |
| Event | 2016 IEEE Intelligent Vehicles Symposium, IV 2016 - Gotenburg, Sweden Duration: 19 Jun 2016 → 22 Jun 2016 |
Publication series
| Name | IEEE Intelligent Vehicles Symposium, Proceedings |
|---|---|
| Volume | 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 subject areas
- Computer Science(all)
- Computer Science Applications
- Engineering(all)
- Automotive Engineering
- Mathematics(all)
- Modelling and Simulation
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2016 IEEE Intelligent Vehicles Symposium, IV 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 194-201 7535385 (IEEE Intelligent Vehicles Symposium, Proceedings; Vol. 2016-August).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -