车辆不传图像特征，只传关键目标位置速度信息，大幅降低通信负担。
From Features to Reference Points: Lightweight and Adaptive Fusion for Cooperative Autonomous Driving
- 用目标的位置、速度等参考点替代特征图传输，实现轻量通信。
- 在M3CAD数据集上通信量下降五数量级，仅需几KB/s。
- 适合异构感知系统间协作，适用于实时大规模自动驾驶场景。
我们提出RefPtsFusion，一种轻量化且可解释的协同自动驾驶框架。与传统共享大尺寸特征图或查询嵌入不同，车辆间交换紧凑的参考点信息，如物体的位置、速度和尺寸。该方法将关注点从“看到了什么”转向“应该看哪里”，构建了与传感器和模型无关的接口，可在异构感知系统间有效工作，并显著降低通信带宽。为丰富共享信息，我们进一步设计了选择性Top-K查询融合机制，仅加入发送方高置信度查询，从而在精度与通信成本间取得良好平衡。在M3CAD数据集上的实验表明，RefPtsFusion在保持稳定感知性能的同时，通信开销相比传统特征级融合方法降低五数量级，从数百MB/s降至5FPS下仅几KB/s。大量实验还验证了其强鲁棒性和一致的传输行为，展现出在可扩展、实时协同驾驶系统中的巨大潜力。
原文摘要 · Abstract (English)
We present RefPtsFusion, a lightweight and interpretable framework for cooperative autonomous driving. Instead of sharing large feature maps or query embeddings, vehicles exchange compact reference points, e.g., objects' positions, velocities, and size information. This approach shifts the focus from "what is seen" to "where to see", creating a sensor- and model-independent interface that works well across vehicles with heterogeneous perception models while greatly reducing communication bandwidth. To enhance the richness of shared information, we further develop a selective Top-K query fusion that selectively adds high-confidence queries from the sender. It thus achieves a strong balance between accuracy and communication cost. Experiments on the M3CAD dataset show that RefPtsFusion maintains stable perception performance while reducing communication overhead by five orders of magnitude, dropping from hundreds of MB/s to only a few KB/s at 5 FPS (frame per second), compared to traditional feature-level fusion methods. Extensive experiments also demonstrate RefPtsFusion's strong robustness and consistent transmission behavior, highlighting its potential for scalable, real-time cooperative driving systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。