Details
| Original language | English |
|---|---|
| Title of host publication | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 |
| Subtitle of host publication | Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1475-1481 |
| Number of pages | 7 |
| ISBN (electronic) | 978-1-5386-7024-8 |
| ISBN (print) | 978-1-5386-7025-5 |
| Publication status | Published - Oct 2019 |
| Event | 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019 - Auckland, New Zealand Duration: 27 Oct 2019 → 30 Oct 2019 |
Abstract
We present a discrete reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm to build lane accurate maps by solving a partition problem. Our algorithm finds lane middle axes by 2D cubic Hermite splines least squares alignment to trajectory clusters. The steps of the rjMCMC optimization are designed to change the number of clusters and the trajectory assignment. Furthermore, we introduce our tracking approach, which is used to generate our trajectory data base by tracking vehicles in Velodyne LiDAR scans at several junctions in Hannover, Germany. We validate our results by comparing them to a manually generated ground truth map, based on LiDAR mobile mapping data, and a manually generated reference map on base of the trajectory data to evaluate our result by ignoring accuracy errors of the tracking process. Finally, we show the transferability of our approach by applying it to datasets from the map construction data base.
ASJC Scopus subject areas
- Computer Science(all)
- Artificial Intelligence
- Decision Sciences(all)
- Management Science and Operations Research
- Physics and Astronomy(all)
- Instrumentation
- Social Sciences(all)
- Transportation
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2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019: Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1475-1481 8917150.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Discrete Reversible Jump Markov Chain Monte Carlo Trajectory Clustering
AU - Busch, Steffen
AU - Brenner, Claus
N1 - Funding information: This work was funded by the German Science Foundation DFG within the priority programme SPP 1835, “Cooperative Interacting Automobiles”.
PY - 2019/10
Y1 - 2019/10
N2 - We present a discrete reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm to build lane accurate maps by solving a partition problem. Our algorithm finds lane middle axes by 2D cubic Hermite splines least squares alignment to trajectory clusters. The steps of the rjMCMC optimization are designed to change the number of clusters and the trajectory assignment. Furthermore, we introduce our tracking approach, which is used to generate our trajectory data base by tracking vehicles in Velodyne LiDAR scans at several junctions in Hannover, Germany. We validate our results by comparing them to a manually generated ground truth map, based on LiDAR mobile mapping data, and a manually generated reference map on base of the trajectory data to evaluate our result by ignoring accuracy errors of the tracking process. Finally, we show the transferability of our approach by applying it to datasets from the map construction data base.
AB - We present a discrete reversible jump Markov Chain Monte Carlo (rjMCMC) algorithm to build lane accurate maps by solving a partition problem. Our algorithm finds lane middle axes by 2D cubic Hermite splines least squares alignment to trajectory clusters. The steps of the rjMCMC optimization are designed to change the number of clusters and the trajectory assignment. Furthermore, we introduce our tracking approach, which is used to generate our trajectory data base by tracking vehicles in Velodyne LiDAR scans at several junctions in Hannover, Germany. We validate our results by comparing them to a manually generated ground truth map, based on LiDAR mobile mapping data, and a manually generated reference map on base of the trajectory data to evaluate our result by ignoring accuracy errors of the tracking process. Finally, we show the transferability of our approach by applying it to datasets from the map construction data base.
UR - http://www.scopus.com/inward/record.url?scp=85076823765&partnerID=8YFLogxK
U2 - 10.1109/ITSC.2019.8917150
DO - 10.1109/ITSC.2019.8917150
M3 - Conference contribution
AN - SCOPUS:85076823765
SN - 978-1-5386-7025-5
SP - 1475
EP - 1481
BT - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019
Y2 - 27 October 2019 through 30 October 2019
ER -