Fast2comm:Collaborative perception combined with prior knowledge

• URL: http://arxiv.org/abs/2505.00740v1
• Date: Wed, 30 Apr 2025 02:32:47 GMT
• Title: Fast2comm:Collaborative perception combined with prior knowledge
• Authors: Zhengbin Zhang, Yan Wu, Hongkun Zhang,
• Abstract summary: We propose Fast2comm, a prior knowledge-based collaborative perception framework.<n>Specifically, we propose a prior-supervised confidence feature generation method, that effectively distinguishes foreground from background.<n>We also propose GT Bounding Box-based spatial prior feature selection strategy, to ensure only the most informative prior-knowledge features are selected and shared.
• Score: 2.2809858115207664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Collaborative perception has the potential to significantly enhance perceptual accuracy through the sharing of complementary information among agents. However, real-world collaborative perception faces persistent challenges, particularly in balancing perception performance and bandwidth limitations, as well as coping with localization errors. To address these challenges, we propose Fast2comm, a prior knowledge-based collaborative perception framework. Specifically, (1)we propose a prior-supervised confidence feature generation method, that effectively distinguishes foreground from background by producing highly discriminative confidence features; (2)we propose GT Bounding Box-based spatial prior feature selection strategy to ensure that only the most informative prior-knowledge features are selected and shared, thereby minimizing background noise and optimizing bandwidth efficiency while enhancing adaptability to localization inaccuracies; (3)we decouple the feature fusion strategies between model training and testing phases, enabling dynamic bandwidth adaptation. To comprehensively validate our framework, we conduct extensive experiments on both real-world and simulated datasets. The results demonstrate the superior performance of our model and highlight the necessity of the proposed methods. Our code is available at https://github.com/Zhangzhengbin-TJ/Fast2comm.

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