Abstract:
The diverse agents in multi-agent perception systems may be from different companies. Each company might use the identical classic neural network architecture based encoder for feature extraction. However, the data source to train the various agents is independent and private in each company, leading to the Distribution Gap of different private data for training distinct agents in multi-agent perception system. The data silos by the above Distribution Gap could result in a significant performance decline in multi-agent perception.
In this paper, we thoroughly examine the impact of the distribution gap on existing multi-agent perception systems. To break the data silos, we introduce the Feature Distribution-aware Aggregation (FDA) framework for cross-domain learning to mitigate the above Distribution Gap in multi-agent perception.
FDA comprises two key components: Learnable Feature Compensation Module and Distribution-aware Statistical Consistency Module, both aimed at enhancing intermediate features to minimize the distribution gap among multi-agent features.
Intensive experiments on the public OPV2V and V2XSet datasets underscore FDA’s effectiveness in point cloud-based 3D object detection, presenting it as an invaluable augmentation to existing multi-agent perception systems.
Expert Commentary:
The problem of data silos and the distribution gap in multi-agent perception systems is a significant challenge in the field. This paper brings attention to this issue and proposes an innovative solution called the Feature Distribution-aware Aggregation (FDA) framework.
The FDA framework is designed to address the distribution gap by introducing two key components: the Learnable Feature Compensation Module and the Distribution-aware Statistical Consistency Module. These components aim to enhance intermediate features and minimize the distribution gap among multi-agent features.
This approach is particularly valuable in scenarios where diverse agents from different companies are involved in a multi-agent perception system. Even if these agents use identical neural network architectures for feature extraction, the private and independent data sources for training each agent can result in significant performance decline due to data silos.
The Learnable Feature Compensation Module and the Distribution-aware Statistical Consistency Module help break down these data silos by enhancing the intermediate features and ensuring consistency among the features extracted by different agents. By minimizing the distribution gap, the FDA framework enables better cooperation and coordination among the agents in a multi-agent perception system.
The effectiveness of the FDA framework is supported by intensive experiments on public datasets such as OPV2V and V2XSet. The positive results obtained in point cloud-based 3D object detection highlight the value of FDA as an augmentation to existing multi-agent perception systems.
In conclusion, the paper highlights the importance of addressing the distribution gap and data silos in multi-agent perception systems. The proposed FDA framework provides a promising solution to mitigate these issues and improve the overall performance of such systems. Further research and implementation of FDA in real-world scenarios are warranted to explore its full potential.