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Collaborative Multimodal Fusion Network for Multiagent Perception
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AI
DOI：10.1109/TCYB.2024.3491756.png)
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Abstract
En 中文
With the increasing popularity of autonomous driving systems and their applications in complex transportation scenarios, collaborative perception among multiple intelligent agents has become an important research direction. Existing single-agent multimodal fusion approaches are limited by their inability to leverage additional sensory data from nearby agents. In this article, we present the collaborative multimodal fusion network (CMMFNet) for distributed perception in multiagent systems. CMMFNet first extracts modality-specific features from LiDAR point clouds and camera images for each agent using dual-stream neural networks. To overcome the ambiguity in-depth prediction, we introduce a collaborative depth supervision module that projects dense fused point clouds onto image planes to generate more accurate depth ground truths. We then present modality-aware fusion strategies to aggregate homogeneous features across agents while preserving their distinctive properties. To align heterogeneous LiDAR and camera features, we introduce a modality consistency learning method. Finally, a transformer-based fusion module dynamically captures cross-modal correlations to produce a unified representation. Comprehensive evaluations on two extensive multiagent perception datasets, OPV2V and V2XSet, affirm the superiority of CMMFNet in detection performance, establishing a new benchmark in the field.
Keywords:
Collaboration
Sensors
Laser radar
Cameras
Feature extraction
Three-dimensional displays
Point cloud compression
Autonomous vehicles
Sensor fusion
Sensor phenomena and characterization
3-D object detection
autonomous driving
collaborative perception
multiagent system
multimodal fusion
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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