用目标查询实现3D检测协作，大幅降低传输开销。
CoopDETR: A Unified Cooperative Perception Framework for 3D Detection via Object Query
- 以目标查询替代区域特征，实现高效信息传递。
- 在OPV2V和V2XSet上达顶尖性能，传输成本降为1/782。
- 适合车联网中低带宽场景下的自动驾驶感知系统。
协同感知通过共享环境信息提升自动驾驶车辆的个体感知能力。然而，如何平衡感知性能与通信开销仍是难题。现有方法传输区域级特征，可解释性差且带宽消耗大，难以实用。本文提出CoopDETR，一种基于目标查询的新型协同感知框架。其包含两个核心模块：单智能体查询生成，将原始传感器数据高效编码为对象查询，降低传输开销并保留关键检测信息；跨智能体查询融合，包含空间查询匹配（SQM）与目标查询聚合（OQA），实现查询间的有效交互。在OPV2V和V2XSet数据集上的实验表明，CoopDETR达到当前最优性能，传输成本仅为此前方法的1/782。
原文摘要 · Abstract (English)
Cooperative perception enhances the individual perception capabilities of autonomous vehicles (AVs) by providing a comprehensive view of the environment. However, balancing perception performance and transmission costs remains a significant challenge. Current approaches that transmit region-level features across agents are limited in interpretability and demand substantial bandwidth, making them unsuitable for practical applications. In this work, we propose CoopDETR, a novel cooperative perception framework that introduces object-level feature cooperation via object query. Our framework consists of two key modules: single-agent query generation, which efficiently encodes raw sensor data into object queries, reducing transmission cost while preserving essential information for detection; and cross-agent query fusion, which includes Spatial Query Matching (SQM) and Object Query Aggregation (OQA) to enable effective interaction between queries. Our experiments on the OPV2V and V2XSet datasets demonstrate that CoopDETR achieves state-of-the-art performance and significantly reduces transmission costs to 1/782 of previous methods.
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