动态带宽下自适应选择时空观测，提升多车协同感知效率
Bandwidth-Adaptive Spatiotemporal Correspondence Identification for Collaborative Perception
- 根据实时带宽动态筛选并共享部分时空观测数据
- 在仿真中实现8%-56%的共视目标检索与传输效率提升
- 适用于带宽波动的自动驾驶等协同感知场景
协同感知中的对应识别（CoID）使多机器人能一致地指代各自视域内的同一物体。在实际应用如车联网自动驾驶中，受限于通信带宽，车辆无法直接共享原始观测数据。为此，本文提出一种带宽自适应的时空对应识别方法，使机器人可按需逐步选择并共享部分时空观测，以适应动态变化的通信约束。我们在车联网自动驾驶模拟场景中评估该方法，实验结果表明其能有效实现CoID，并适应动态带宽变化。相比先前方法，本方案在共视目标检索和数据共享效率上整体提升8%-56%，达到当前最优性能。
原文摘要 · Abstract (English)
Correspondence identification (CoID) is an essential capability in multi-robot collaborative perception, which enables a group of robots to consistently refer to the same objects within their respective fields of view. In real-world applications, such as connected autonomous driving, vehicles face challenges in directly sharing raw observations due to limited communication bandwidth. In order to address this challenge, we propose a novel approach for bandwidth-adaptive spatiotemporal CoID in collaborative perception. This approach allows robots to progressively select partial spatiotemporal observations and share with others, while adapting to communication constraints that dynamically change over time. We evaluate our approach across various scenarios in connected autonomous driving simulations. Experimental results validate that our approach enables CoID and adapts to dynamic communication bandwidth changes. In addition, our approach achieves 8%-56% overall improvements in terms of covisible object retrieval for CoID and data sharing efficiency, which outperforms previous techniques and achieves the state-of-the-art performance. More information is available at: https://gaopeng5.github.io/acoid.
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