多智能体协作感知框架，高效通信且抗误差，提升车辆感知性能。
mmCooper: A Multi-agent Multi-stage Communication-efficient and Collaboration-robust Cooperative Perception Framework
- 分阶段动态融合信息，平衡精度与通信开销。
- 过滤低置信度信息，修复合作方检测结果，提升准确性。
- 适合车联网、自动驾驶等对鲁棒性要求高的场景。
协同感知通过智能体间共享感知信息显著提升单车感知性能。然而，实际部署面临带宽限制和信息交换中的校准误差问题。为此，我们提出mmCooper——一种新型多智能体、多阶段、通信高效且协作鲁棒的协同感知框架。该框架采用分阶段协作策略，动态自适应地平衡中间与后期信息共享，在保持通信效率的同时提升感知性能。为应对潜在的错位与校准误差，框架阻止低置信度感知信息的传输，并对合作方传来的检测结果进行优化，以提高精度。在真实世界和模拟数据集上的广泛评估结果验证了mmCooper框架及其组件的有效性。
原文摘要 · Abstract (English)
Collaborative perception significantly enhances individual vehicle perception performance through the exchange of sensory information among agents. However, real-world deployment faces challenges due to bandwidth constraints and inevitable calibration errors during information exchange. To address these issues, we propose mmCooper, a novel multi-agent, multi-stage, communication-efficient, and collaboration-robust cooperative perception framework. Our framework leverages a multi-stage collaboration strategy that dynamically and adaptively balances intermediate- and late-stage information to share among agents, enhancing perceptual performance while maintaining communication efficiency. To support robust collaboration despite potential misalignments and calibration errors, our framework prevents misleading low-confidence sensing information from transmission and refines the received detection results from collaborators to improve accuracy. The extensive evaluation results on both real-world and simulated datasets demonstrate the effectiveness of the mmCooper framework and its components.
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