Details
| Original language | English |
|---|---|
| Title of host publication | IWCTS 2012 - 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science |
| Pages | 39-42 |
| Number of pages | 4 |
| Publication status | Published - Nov 2012 |
| Event | 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2012 - Redondo Beach, CA, United States Duration: 6 Nov 2012 → 6 Nov 2012 |
Publication series
| Name | IWCTS 2012 - 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science |
|---|
Abstract
In this paper we deal with a strategy for a collaborative positioning of vehicles to improve their ego positioning capabilities. One way to achieve this is the sharing of the vehicle's own position and additional measurements to vehicles with known position in their surrounding area. Under the assumption that a single vehicle is able to obtain its ego position by on-board sensors (like laser scanners and GNSS equipment) and in combination with available landmark maps, the consideration of additional measurements to other vehicles leads to a position improvement especially in case of sparse landmark maps. Based on an available landmark map covering built-up areas and highway-like roads, a set of simulations is carried out to evaluate the resulting improvement by using relative position data among nearby vehicles. Different kinds of collaborative positioning scenarios are investigated and contrasted with ego positioning using only the landmark map.
Keywords
- autonomous vehicles, collaborative positioning, feature extraction, landmark based maps, localization
ASJC Scopus subject areas
- Computer Science(all)
- Computational Theory and Mathematics
- Social Sciences(all)
- Transportation
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IWCTS 2012 - 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science. 2012. p. 39-42 (IWCTS 2012 - 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Collaborative positioning using landmark maps
AU - Paffenholz, Jens André
AU - Brenner, Claus
AU - Sester, Monika
PY - 2012/11
Y1 - 2012/11
N2 - In this paper we deal with a strategy for a collaborative positioning of vehicles to improve their ego positioning capabilities. One way to achieve this is the sharing of the vehicle's own position and additional measurements to vehicles with known position in their surrounding area. Under the assumption that a single vehicle is able to obtain its ego position by on-board sensors (like laser scanners and GNSS equipment) and in combination with available landmark maps, the consideration of additional measurements to other vehicles leads to a position improvement especially in case of sparse landmark maps. Based on an available landmark map covering built-up areas and highway-like roads, a set of simulations is carried out to evaluate the resulting improvement by using relative position data among nearby vehicles. Different kinds of collaborative positioning scenarios are investigated and contrasted with ego positioning using only the landmark map.
AB - In this paper we deal with a strategy for a collaborative positioning of vehicles to improve their ego positioning capabilities. One way to achieve this is the sharing of the vehicle's own position and additional measurements to vehicles with known position in their surrounding area. Under the assumption that a single vehicle is able to obtain its ego position by on-board sensors (like laser scanners and GNSS equipment) and in combination with available landmark maps, the consideration of additional measurements to other vehicles leads to a position improvement especially in case of sparse landmark maps. Based on an available landmark map covering built-up areas and highway-like roads, a set of simulations is carried out to evaluate the resulting improvement by using relative position data among nearby vehicles. Different kinds of collaborative positioning scenarios are investigated and contrasted with ego positioning using only the landmark map.
KW - autonomous vehicles
KW - collaborative positioning
KW - feature extraction
KW - landmark based maps
KW - localization
UR - http://www.scopus.com/inward/record.url?scp=84875167342&partnerID=8YFLogxK
U2 - 10.1145/2442942.2442950
DO - 10.1145/2442942.2442950
M3 - Conference contribution
AN - SCOPUS:84875167342
SN - 9781450316934
T3 - IWCTS 2012 - 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science
SP - 39
EP - 42
BT - IWCTS 2012 - 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science
T2 - 5th ACM SIGSPATIAL International Workshop on Computational Transportation Science, IWCTS 2012
Y2 - 6 November 2012 through 6 November 2012
ER -