多智能体协同感知中，用专家机制提升检测与分割的鲁棒性。
CoDS: Robust Collaborative Perception via Expert-driven Detection and BEV Segmentation
- 引入可靠性地图动态评估融合特征质量，指导不同任务需求。
- 在OPV2V和V2V4Real数据集上，检测与分割性能均超越现有方法。
- 适合自动驾驶中复杂噪声环境下高可靠感知系统研发者。
协同感知通过多智能体信息交互突破单视角局限，但姿态误差、通信延迟等多源噪声会降低融合特征质量，制约感知性能。联合训练检测与鸟瞰图（BEV）分割可自然缓解此问题：分割结果约束目标分布，检测框辅助恢复模糊的边界。为此，本文提出专家驱动的协同感知框架CoDS。为解决融合质量的空间不一致性，首先引入协同可靠性地图（CoRM），显式量化特征质量分布；基于CoRM，设计语义混合专家（S-MoE）模块，针对不同特征需求提取差异化特征；最后，通过双向任务互补交互（BTCI）模块，以双向注入方式优化任务感知特征。在OPV2V和V2V4Real数据集上的大量实验表明，CoDS在两项任务上均优于现有基线，并在多源噪声下保持稳定鲁棒性。代码已开源。
原文摘要 · Abstract (English)
Collaborative perception breaks through single-view limitations via multi-agent information exchange. However, multi-source noise such as pose errors and communication delays degrades fusion feature quality, constraining perception performance. Joint training of detection and BEV segmentation provides a natural remedy, where segmented road regions help constrain target distributions and detection bounding boxes help recover ambiguous segmentation boundaries. To this end, we propose a robust Collaborative perception framework with expert-driven Detection and bev Segmentation (CoDS). To address spatial inconsistency in fusion quality, we first introduce the Collaborative Reliability Map (CoRM) to explicitly quantify feature quality distribution. Based on CoRM, we design the Semantic Mixture-of-Experts (S-MoE) module to extract differentiated features for inconsistent feature demands. Finally, to further mitigate feature noise degradation, the Bidirectional Task Complementary Interaction (BTCI) refines task-aware features through bidirectional injection. Extensive experiments on OPV2V and V2V4Real datasets show that our CoDS surpasses existing baselines on both tasks and maintains stable robustness under multi-source noise. Code: https://github.com/JinlongW128/CoDS and https://openi.pcl.ac.cn/OpenAIDriving/CoDS.
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