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Title:

TUMTraf V2X Cooperative Perception Dataset

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Zimmer, Walter; Wardana, Gerhard Arya; Sritharan, Suren; Zhou, Xingcheng; Song, Rui; Knoll, Alois C.
Pages contribution:
21
Abstract:
Cooperative perception offers several benefits for enhancing the capabilities of autonomous vehicles and improving road safety. Using roadside sensors in addition to onboard sensors increases reliability and extends the sensor range. External sensors offer higher situational awareness for automated vehicles and prevent occlusions. We propose CoopDet3D, a cooperative multi-modal fusion model, and TUMTraf-V2X, a perception dataset, for the cooperative 3D object detection and tracking task. Our dat...     »
Keywords:
Autonomous Driving, Deep Learning, 3D Object Detection, Tracking, Camera-LiDAR Fusion, Cooperative Perception, Vehicle-Infrastructure Fusion, Roadside sensors, Camera, LiDAR, V2X, ITS, Deep Fusion, Labeling, Dataset, TUMTraf
Dewey Decimal Classification:
000 Informatik, Wissen, Systeme
Book / Congress title:
2024 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
Edition:
2024
Date of congress:
17.06.2024
Publisher:
IEEE/CVF
Date of publication:
03.03.2024
Year:
2024
Quarter:
1. Quartal
Year / month:
2024-03
Month:
Mar
Pages:
21
Reviewed:
ja
Language:
en
Publication format:
WWW
TUM Institution:
Chair of Robotics, Artificial Intelligence and Real-time Systems
Format:
Text
 BibTeX