无需提前准备，让独立训练的智能体实时协作感知，性能提升超30点。
BOLT: Online Lightweight Adaptation for Preparation-Free Heterogeneous Cooperative Perception
- 用自身检测结果做教师指导，在线适配邻居特征。
- 仅需0.9M参数，在无预训练条件下提升32.3个AP@50点。
- 适合跨厂商、临时协作的自动驾驶场景使用。
现有异构协同感知方法依赖离线联合训练或定制化模型适配等前期准备，但在真实场景中难以实现，因各智能体由不同开发者独立训练且仅偶尔在线相遇。本文研究无需准备的异构协同感知，即各智能体使用独立训练的单机检测器，无需部署前协调。我们发现在此设定下直接融合邻居特征表现远低于仅依赖自身感知。为此提出BOLT，一种轻量级即插即用模块，通过以自身检测结果为教师进行在线特征适配，仅需自身预测结果而无需真实标签。BOLT利用高置信度自身感知特征引导跨智能体特征域对齐，同时使邻居在自身低置信区域贡献特征。仅含0.9M可训练参数，BOLT在无预准备条件下相较原始未适配融合最高提升32.3个AP@50点，在DAIR-V2X与OPV2V数据集上持续超越仅依赖自身感知的结果，适用于不同编码器组合与融合策略。代码开源：https://github.com/sidiangongyuan/BOLT。
原文摘要 · Abstract (English)
Most existing heterogeneous cooperative perception methods depend on prior preparation like offline joint training or tailored collaborator-model adaptation. Such preprocessing is, however, generally impractical in real scenarios, as agents are usually independently trained by different developers and meet occasionally online. This work investigates \emph{preparation-free heterogeneous cooperative perception}, where agents use independently trained single-agent detectors without any pre-deployment coordination. We find direct cross-agent fusion under this setting greatly underperforms ego-only perception. We present BOLT, a lightweight plug-and-play module that adapts neighboring features online via ego-as-teacher distillation, requiring only ego predictions without ground-truth labels. BOLT leverages high-confidence ego perception features to guide cross-agent feature-domain alignment, while enabling neighbors to contribute features in the ego's low-confidence regions. With only 0.9M trainable parameters, BOLT improves AP@50 by up to 32.3 points over vanilla unadapted fusion in the preparation-free setting. It consistently outperforms ego-only results on DAIR-V2X and OPV2V, across different encoder pairs and fusion strategies. Code: https://github.com/sidiangongyuan/BOLT.
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