A Late Collaborative Perception Framework for 3D Multi-Object and Multi-Source Association and Fusion
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
In autonomous driving, recent research has increasingly focused on
collaborative perception based on deep learning to overcome the limitations of
individual perception systems. Although these methods achieve high accuracy,
they rely on high communication bandwidth and require unrestricted access to
each agent's object detection model architecture and parameters. These
constraints pose challenges real-world autonomous driving scenarios, where
communication limitations and the need to safeguard proprietary models hinder
practical implementation. To address this issue, we introduce a novel late
collaborative framework for 3D multi-source and multi-object fusion, which
operates solely on shared 3D bounding box attributes-category, size, position,
and orientation-without necessitating direct access to detection models. Our
framework establishes a new state-of-the-art in late fusion, achieving up to
five times lower position error compared to existing methods. Additionally, it
reduces scale error by a factor of 7.5 and orientation error by half, all while
maintaining perfect 100% precision and recall when fusing detections from
heterogeneous perception systems. These results highlight the effectiveness of
our approach in addressing real-world collaborative perception challenges,
setting a new benchmark for efficient and scalable multi-agent fusion.
