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Interest in cooperative perception is growing quickly due to its remarkable performance in improving perception capabilities for connected and automated vehicles. This improvement is crucial, especially for automated driving scenarios in which perception performance is one of the main bottlenecks to the development of safety and efficiency. However, current cooperative perception methods typically assume that all collaborating vehicles have enough communication bandwidth to share all features with an identical spatial size, which is impractical for real-world scenarios. In this paper, we propose Adaptive Cooperative Perception, a new cooperative perception framework that is not limited by the aforementioned assumptions, aiming to enable cooperative perception under more realistic and challenging conditions. To support this, a novel feature encoder is proposed and named Pillar Attention Encoder. A pillar attention mechanism is designed to extract the feature data while considering its significance for the perception task. An adaptive feature filter is proposed to adjust the size of the feature data for sharing by considering the importance value of the feature. Experiments are conducted for cooperative object detection from multiple vehicle-based and infrastructure-based LiDAR sensors under various communication conditions. Results demonstrate that our method can successfully handle dynamic communication conditions and improve the mean Average Precision by 10.18% when compared with the state-of-the-art feature encoder.
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This content will become publicly available on June 9, 2026
CoOpTex: Multimodal Cooperative Perception and Task Execution in Time-Critical Distributed Autonomous System
Integrating multimodal data such as RGB and LiDAR from multiple views significantly increases computational and communication demands, which can be challenging for resource-constrained autonomous agents while meeting the time-critical deadlines required for various mission-critical applications. To address this challenge, we propose CoOpTex, a collaborative task execution framework designed for cooperative perception in distributed autonomous systems (DAS). CoOpTex contribution is twofold: (a) CoOpTex fuses multiview RGB images to create a panoramic camera view for 2D object detection and utilizes 360° LiDAR for 3D object detection, improving accuracy with a lightweight Graph Neural Network (GNN) that integrates object coordinates from both perspectives, (b) To optimize task execution and meet the deadline, CoOpTex dynamically offloads computationally intensive image stitching tasks to auxiliary devices when available and adjusts frame capture rates for RGB frames based on device mobility and processing capabilities. We implement CoOpTex in real-time on static and mobile heterogeneous autonomous agents, which helps to significantly reduce deadline violations by 100% while improving frame rates for 2D detection by 2.2 times in stationary and 2 times in mobile conditions, demonstrating its effectiveness in enabling real-time cooperative perception.
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- Award ID(s):
- 2233879
- PAR ID:
- 10637218
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3315-4372-3
- Page Range / eLocation ID:
- 195 to 202
- Format(s):
- Medium: X
- Location:
- Lucca, Italy
- Sponsoring Org:
- National Science Foundation
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