Name
Title | ||
|---|---|---|
Car2X-based perception in a high-level fusion architecture for cooperative perception systems |
Abstract | ||
|---|---|---|
In cooperative perception systems, different vehicles share object data obtained by their local environment perception sensors, like radar or lidar, via wireless communication. In this paper, this so-called Car2X-based perception is modeled as a virtual sensor in order to integrate it into a highlevel sensor data fusion architecture. The spatial and temporal alignment of incoming data is a major issue in cooperative perception systems. Temporal alignment is done by predicting the received object data with a model-based approach. In this context, the CTRA (constant turn rate and acceleration) motion model is used for a three-dimensional prediction of the communication partner's motion. Concerning the spatial alignment, two approaches to transform the received data, including the uncertainties, into the receiving vehicle's local coordinate frame are compared. The approach using an unscented transformation is shown to be superior to the approach by linearizing the transformation function. Experimental results prove the accuracy and consistency of the virtual sensor's output. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IVS.2012.6232130 | Intelligent Vehicles Symposium |
Keywords | Field | DocType |
cooperative communication,radiocommunication,road vehicles,sensor fusion,traffic information systems,CTRA,Car2X-based perception,communication partner motion,constant turn rate and acceleration motion model,cooperative perception systems,high-level fusion architecture,highlevel sensor data fusion architecture,incoming data,lidar,local environment perception sensors,model-based approach,radar,received data,received object data,receiving vehicle local coordinate frame,spatial alignment,temporal alignment,three-dimensional prediction,transformation function,unscented transformation,virtual sensor output,wireless communication | Radar,Computer vision,Wireless,Transformation (function),Kalman filter,Sensor fusion,Acceleration,Artificial intelligence,Covariance matrix,Engineering,Perception | Conference |
ISSN | ISBN | Citations |
1931-0587 | 978-1-4673-2119-8 | 15 |
PageRank | References | Authors |
0.91 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Rauch | 1 | 38 | 3.43 |
| Klanner, F. | 2 | 107 | 8.09 |
| Ralph H. Rasshofer | 3 | 17 | 1.68 |
| Klaus Dietmayer | 4 | 822 | 102.64 |