Authors: Nicolas Baumann, Michael Baumgartner, Edoardo Ghignone, Jonas Kühne, Tobias Fischer, Yung-Hsu Yang, Marc Pollefeys, Michele Magno
Published on: March 22, 2024
Impact Score: 7.4
Arxiv code: Arxiv:2403.15313
Summary
- What is new: Combination of RADARs and cameras for 3D detection and tracking in self-driving vehicles, building upon the BEVDet architecture.
- Why this is important: The need for accurate object detection and tracking in autonomous vehicles, without the high costs associated with LiDAR sensors.
- What the research proposes: Camera-RADAR 3D Detection and Tracking (CR3DT), which integrates RADAR’s spatial and velocity information into a camera-based detection system.
- Results: An increase of 5.3% in detection performance (mAP) and 14.9% in tracking accuracy (AMOTA) compared to camera-only solutions.
Technical Details
Technological frameworks used: BEVDet architecture
Models used: CR3DT
Data used: nuScenes dataset
Potential Impact
Automotive industry, particularly companies focusing on autonomous driving technologies and self-driving vehicle manufacturers.
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