Name
Title | ||
|---|---|---|
Object perception for intelligent vehicle applications: A multi-sensor fusion approach |
Abstract | ||
|---|---|---|
The paper addresses the problem of object perception for intelligent vehicle applications with main tasks of detection, tracking and classification of obstacles where multiple sensors (i.e.: lidar, camera and radar) are used. New algorithms for raw sensor data processing and sensor data fusion are introduced making the most information from all sensors in order to provide a more reliable and accurate information about objects in the vehicle environment. The proposed object perception module is implemented and tested on a demonstrator car in real-life traffics and evaluation results are presented. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IVS.2014.6856588 | Intelligent Vehicles Symposium |
Keywords | Field | DocType |
obstacle tracking,multisensor fusion,raw sensor data processing,obstacle classification,traffic engineering computing,obstacle detection,object tracking,object detection,intelligent vehicle,intelligent transportation systems,sensor data fusion,object perception,sensor fusion | Radar,Object detection,Computer vision,Data processing,Radar tracker,Sensor fusion,Vehicle dynamics,Video tracking,Artificial intelligence,Intelligent transportation system,Engineering | Conference |
ISSN | Citations | PageRank |
1931-0587 | 8 | 0.52 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Trung-Dung Vu | 1 | 76 | 6.57 |
| Olivier Aycard | 2 | 309 | 26.57 |
| Fabio Tango | 3 | 54 | 7.74 |