Model-Agnostic Multi-Agent Perception Framework
Model-Agnostic Multi-Agent Perception Framework
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DOI：
10.1109/icra48891.2023.10161460
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Weizhe (Wesley) Chen;Runsheng Xu;Hao Xiang;Lantao Liu;Jiaqi Ma
中科院分区：
文献类型：
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作者：
Weizhe (Wesley) Chen;Runsheng Xu;Hao Xiang;Lantao Liu;Jiaqi Ma
现有的多智能体感知系统假设每个智能体使用相同的模型，具有相同的参数和结构。由于不同感知模型的置信度不匹配，性能可能会降低。在这项工作中，我们提出了一个模型无关的多智能体感知框架，以减少模型差异所造成的负面影响，而不共享模型信息。具体来说，我们提出了一个置信校准器，可以消除预测置信得分偏差。每个代理都在标准的公共数据库上独立进行这种校准，以保护知识产权。我们还提出了一个相应的包围盒聚合算法，考虑了相邻框的置信度和空间一致性。我们的实验揭示了在不同的代理模型校准的必要性，结果表明，所提出的框架提高了基线的异构代理的3D对象检测性能。代码可以在这个URL上找到。
Existing multi-agent perception systems assume that every agent utilizes the same model with identical parameters and architecture. The performance can be degraded with different perception models due to the mismatch in their confidence scores. In this work, we propose a model-agnostic multi-agent perception framework to reduce the negative effect caused by the model discrepancies without sharing the model information. Specifically, we propose a confidence calibrator that can eliminate the prediction confidence score bias. Each agent performs such calibration independently on a standard public database to protect intellectual property. We also propose a corresponding bounding box aggregation algorithm that considers the confidence scores and the spatial agreement of neighboring boxes. Our experiments shed light on the necessity of model calibration across different agents, and the results show that the proposed framework improves the baseline 3D object detection performance of heterogeneous agents. The code can be found at this url.