用雷达多普勒引导稀疏通信，大幅降低自动驾驶协同感知带宽消耗
SlimComm: Doppler-Guided Sparse Queries for Bandwidth-Efficient Cooperative 3-D Perception
- 结合雷达多普勒信息动态生成参考与探索两类查询
- 仅传输关键区域特征，带宽降低90%仍保持高精度
- 适合车联网中资源受限的实时协同感知场景
协同感知使联网自动驾驶汽车通过共享中间特征克服遮挡和传感器范围限制。然而，传输密集的俯视图（BEV）特征图会超出车辆间通信带宽。本文提出SlimComm，一种通信高效的框架，融合4D雷达多普勒与查询驱动的稀疏机制。SlimComm构建以运动为中心的动态地图，区分动静态物体，并生成两类查询：(i) 针对动态与高置信度区域的参考查询；(ii) 通过两阶段偏移探测遮挡区域的探索查询。仅交换特定查询的BEV特征，并通过多尺度门控可变形注意力融合，显著降低数据负载同时保持精度。我们发布基于CARLA的OPV2V-R与Adver-City-R数据集，包含每点雷达多普勒信息。SlimComm在不同交通密度与遮挡条件下，相比先前基线实现高达90%的带宽节省，性能相当或更优。代码与数据集将公开于：https://url.fzi.de/SlimComm。
原文摘要 · Abstract (English)
Collaborative perception allows connected autonomous vehicles (CAVs) to overcome occlusion and limited sensor range by sharing intermediate features. Yet transmitting dense Bird's-Eye-View (BEV) feature maps can overwhelm the bandwidth available for inter-vehicle communication. We present SlimComm, a communication-efficient framework that integrates 4D radar Doppler with a query-driven sparse scheme. SlimComm builds a motion-centric dynamic map to distinguish moving from static objects and generates two query types: (i) reference queries on dynamic and high-confidence regions, and (ii) exploratory queries probing occluded areas via a two-stage offset. Only query-specific BEV features are exchanged and fused through multi-scale gated deformable attention, reducing payload while preserving accuracy. For evaluation, we release OPV2V-R and Adver-City-R, CARLA-based datasets with per-point Doppler radar. SlimComm achieves up to 90% lower bandwidth than full-map sharing while matching or surpassing prior baselines across varied traffic densities and occlusions. Dataset and code will be available at: https://url.fzi.de/SlimComm.
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