In the domain of intelligent transportation systems (ITS), collaborative perception has emerged as a promising approach to overcome the limitations of individual perception by enabling multiple agents to exchange information, thus enhancing their situational awareness. Collaborative perception overcomes the limitations of individual sensors, allowing connected agents to perceive environments beyond their line-of-sight and field of view. However, the reliability of collaborative perception heavily depends on the data aggregation strategy and communication bandwidth, which must overcome the challenges posed by limited network resources. To improve the precision of object detection and alleviate limited network resources, we propose an intermediate collaborative perception solution in the form of a graph attention network (GAT). The proposed approach develops an attention-based aggregation strategy to fuse intermediate representations exchanged among multiple connected agents. This approach adaptively highlights important regions in the intermediate feature maps at both the channel and spatial levels, resulting in improved object detection precision. We propose a feature fusion scheme using attention-based architectures and evaluate the results quantitatively in comparison to other state-of-the-art collaborative perception approaches. Our proposed approach is validated using the V2XSim dataset. The results of this work demonstrate the efficacy of the proposed approach for intermediate collaborative perception in improving object detection average precision while reducing network resource usage.

翻译：在智能交通系统（ITS）领域，协同感知已成为一种有前景的方法，通过使多个智能体交换信息来克服个体感知的局限性，从而增强其态势感知能力。协同感知克服了单个传感器的局限，使互联智能体能够感知超出其视线和视场范围的环境。然而，协同感知的可靠性高度依赖于数据聚合策略和通信带宽，这必须克服有限网络资源带来的挑战。为了提高目标检测精度并缓解网络资源有限的问题，我们提出了一种以图注意力网络（GAT）为形式的中间协同感知方案。所提出的方法开发了一种基于注意力的聚合策略，用于融合多个互联智能体之间交换的中间表示。该方法能够在通道和空间层面上自适应地突出中间特征图中的重要区域，从而提升目标检测精度。我们提出了一种基于注意力架构的特征融合方案，并与当前最先进的协同感知方法进行了定量对比评估。所提出的方法使用V2XSim数据集进行了验证。研究结果证明了所提方法在提高目标检测平均精度的同时减少网络资源使用方面，对中间协同感知的有效性。