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Titel:

TUMTraf V2X Cooperative Perception Dataset

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Zimmer, Walter; Wardana, Gerhard Arya; Sritharan, Suren; Zhou, Xingcheng; Song, Rui; Knoll, Alois C.
Seitenangaben Beitrag:
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...     »
Stichworte:
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-Dezimalklassifikation:
000 Informatik, Wissen, Systeme
Kongress- / Buchtitel:
2024 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)
Ausgabe:
2024
Datum der Konferenz:
17.06.2024
Verlag / Institution:
IEEE/CVF
Publikationsdatum:
03.03.2024
Jahr:
2024
Quartal:
1. Quartal
Jahr / Monat:
2024-03
Monat:
Mar
Seiten:
21
Reviewed:
ja
Sprache:
en
Erscheinungsform:
WWW
TUM Einrichtung:
Chair of Robotics, Artificial Intelligence and Real-time Systems
Format:
Text
 BibTeX