BOLT：面向无准备异构协作感知的在线轻量适应
BOLT: Online Lightweight Adaptation for Preparation-Free Heterogeneous Cooperative Perception
AI总结：
本研究提出BOLT模块，通过 ego-as-teacher 蒸馏实现在线特征域对齐，无需预训练协调，在无准备场景下提升AP@50达32.3点，优于 ego-only 结果。
摘要：
现有异构协作感知方法依赖离线联合训练或定制化协作模型适应，但实际场景中由于不同开发者独立训练且偶遇在线，此类预处理不现实。本文研究无准备异构协作感知，其中代理使用独立训练的单代理检测器。发现直接跨代理融合在该设置下显著劣于单代理感知。我们提出BOLT，一个轻量级即插即用模块，通过 ego-as-teacher 蒸馏在线适应邻近特征，仅需ego预测而无需地面真实标签。BOLT利用高置信度ego感知特征引导跨代理特征域对齐，同时使邻居在ego低置信度区域贡献特征。BOLT仅需0.9M可训练参数，在无准备设置下将AP@50提升至最高32.3点，优于 vanilla unadapted fusion。在DAIR-V2X和OPV2V上，BOLT在不同编码器对和融合策略下均优于ego-only结果。代码：https://github.com/sidiangongyuan/BOLT。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
