基于鸟瞰图流的异步鲁棒协同感知
Asynchrony-Robust Collaborative Perception via Bird's Eye View Flow
AI总结：
针对多智能体协同感知中的时间异步导致信息不匹配的问题，提出基于BEV流的CoBEVFlow系统，通过运动补偿对齐异步消息，构建IRV2V数据集验证，其性能优于基线且极端异步下鲁棒。
摘要：
协同感知可通过促进多智能体间的通信，大幅提升每个智能体的感知能力。但在现实世界中，由于通信延迟、中断和时钟偏移，智能体间的时间异步性不可避免。该问题会导致多智能体融合过程中出现信息不匹配，严重动摇协同的基础。为解决这一问题，我们提出CoBEVFlow——一种基于鸟瞰图（BEV）流的异步鲁棒协同感知系统。CoBEVFlow的核心思路是通过补偿运动来对齐多个智能体发送的异步协同消息。为建模场景中的运动，我们提出BEV流，它是对应每个空间位置的运动向量集合。基于BEV流，异步感知特征可被重新分配到合适的位置，从而减轻异步性的影响。CoBEVFlow有两大优势：（i）CoBEVFlow无需离散化，即可处理在不规则连续时间戳下发送的异步协同消息；（ii）借助BEV流，CoBEVFlow仅传输原始感知特征，而非生成新的感知特征，避免了额外噪声。为验证CoBEVFlow的有效性，我们构建了IRregular V2V（IRV2V）——首个包含多种时间异步情况、模拟不同现实场景的合成协同感知数据集。在IRV2V和真实世界数据集DAIR-V2X上开展的大量实验表明，CoBEVFlow的性能始终优于其他基线方法，且在极端异步设置下仍具备鲁棒性。代码已开源至https://github.com/MediaBrain-SJTU/CoBEVFlow。
英文摘要：
Collaborative perception can substantially boost each agent's perception ability by facilitating communication among multiple agents. However, temporal asynchrony among agents is inevitable in the real world due to communication delays, interruptions, and clock misalignments. This issue causes information mismatch during multi-agent fusion, seriously shaking the foundation of collaboration. To address this issue, we propose CoBEVFlow, an asynchrony-robust collaborative perception system based on bird's eye view (BEV) flow. The key intuition of CoBEVFlow is to compensate motions to align asynchronous collaboration messages sent by multiple agents. To model the motion in a scene, we propose BEV flow, which is a collection of the motion vector corresponding to each spatial location. Based on BEV flow, asynchronous perceptual features can be reassigned to appropriate positions, mitigating the impact of asynchrony. CoBEVFlow has two advantages: (i) CoBEVFlow can handle asynchronous collaboration messages sent at irregular, continuous time stamps without discretization; and (ii) with BEV flow, CoBEVFlow only transports the original perceptual features, instead of generating new perceptual features, avoiding additional noises. To validate CoBEVFlow's efficacy, we create IRregular V2V(IRV2V), the first synthetic collaborative perception dataset with various temporal asynchronies that simulate different real-world scenarios. Extensive experiments conducted on both IRV2V and the real-world dataset DAIR-V2X show that CoBEVFlow consistently outperforms other baselines and is robust in extremely asynchronous settings. The code is available at https://github.com/MediaBrain-SJTU/CoBEVFlow.
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