Name
Title | ||
|---|---|---|
An Approach to Vehicle Trajectory Prediction Using Automatically Generated Traffic Maps |
Abstract | ||
|---|---|---|
Trajectory and intention prediction of traffic participants is an important task in automated driving and crucial for safe interaction with the environment. In this paper, we present a new approach to vehicle trajectory prediction based on automatically generated maps containing statistical information about the behavior of traffic participants in a given area. These maps are generated based on trajectory observations using image processing and map matching techniques. The generated maps contain all typical vehicle movements and probabilities in the considered area. Our prediction approach matches an observed trajectory to a behavior contained in the map and uses this information to generate a prediction. We evaluated our approach on a dataset containing over 14000 trajectories and found that it produces significantly more precise mid-term predictions compared to motion model-based prediction approaches. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/IVS.2018.8500535 | 2018 IEEE Intelligent Vehicles Symposium (IV) |
Keywords | DocType | Volume |
vehicle trajectory prediction,generated traffic maps,intention prediction,traffic participants,automatically generated maps,trajectory observations,prediction approach,mid-term predictions,motion model-based prediction approaches,vehicle movements,image processing,map matching,probability | Conference | abs/1802.08632 |
ISSN | ISBN | Citations |
1931-0587 | 978-1-5386-4453-9 | 0 |
PageRank | References | Authors |
0.34 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jannik Quehl | 1 | 2 | 0.72 |
| Haohao Hu | 2 | 2 | 1.05 |
| Sascha Wirges | 3 | 4 | 1.80 |
| Martin Lauer | 4 | 21 | 8.98 |