发表机构
Michigan State University（密歇根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PEARL提出轻量级提示嵌入框架，通过并行训练稀疏检测与密集域不变解释器实时选择，解决异构协同感知的领域差异，提升AP 8.2%并降低通信成本34.7倍。
AI 中文摘要
协同感知（CP）智能体之间的异构性是新兴协同感知框架面临的主要挑战，这是由于不同传感器、架构和训练数据导致的领域差异。先前的工作通过模型重训练或按智能体类型解释器在统一空间中对齐特征来缓解这一挑战。这些策略（a）需要访问邻居配置，（b）不能完全解决实时协同感知部署问题，并且（c）对运行时加入的未见智能体泛化能力差。为了克服这些挑战，我们提出了PEARL，一种用于匿名和实时轻量级异构协同感知的提示嵌入框架。PEARL支持多个协同感知解释器，并使用两个并行训练的轻量级多尺度解释器为新加入的智能体实时选择一个解释器：一个稀疏检测（LWSD）解释器，用于对齐协同检测的显著区域；以及一个密集、域不变（LWDDI）解释器，产生智能体不变特征以快速选择解释器。两个解释器都使用低秩视觉提示来减少计算、存储和模型复杂度。在模拟（OPV2V、V2XSet）和真实（DAIR-V2X）数据集上的大量实验表明，PEARL能够泛化到模拟和真实世界的协同驾驶场景。其实时模型选择策略相较于随机选择基线获得了8.2%的平均精度（AP）提升，同时平均运行时间为1.67毫秒。虽然PEARL主要为实时协同感知设计，但在传统离线训练下，其平均AP比最先进的异构协同感知框架高出5.6%，同时通信成本降低高达34.7倍。同样重要的是，PEARL不需要共享智能体的配置或模型设置，从而保护可能具有专有性或私密性的信息。这些结果确立了PEARL作为异构协同感知的可扩展且实用的框架。
英文摘要
Heterogeneity across Collaborative Perception (CP) agents is a major challenge for emerging CP frameworks due to domain gaps from differing sensors, architectures, and training data. Prior works mitigate this challenge by aligning features in a unified space via model retraining or per-agent-type interpreters. These strategies (a) require access to neighbor configurations, (b) do not fully address real-time CP deployment, and (c) generalize poorly to unseen agents joining at run time. To overcome these challenges, we present PEARL, a Prompt-Embedding framework for Anonymous and Real-time Lightweight heterogeneous CP. PEARL supports multiple CP interpreters and selects one for a new-joining agent in real time using two lightweight, multi-scale interpreters trained in parallel: a sparse-detection (LWSD) interpreter that aligns salient regions for cooperative detection, and a dense, domain-invariant (LWDDI) interpreter that produces agent-invariant features for fast interpreter selection. Both interpreters use low-rank visual prompts to reduce computation, storage, and model complexity. Extensive experiments on simulated (OPV2V, V2XSet) and real (DAIR-V2X) datasets show that PEARL generalizes across simulated and real-world cooperative driving scenarios. Its real-time model-selection strategy yields an 8.2% Average Precision (AP) gain over a random-selection baseline while running in 1.67 ms on average. Although primarily designed for real-time CP, PEARL also outperforms state-of-the-art heterogeneous CP frameworks under traditional offline training by 5.6% AP on average while reducing communication cost by up to 34.7 times. Equally important, PEARL does not require sharing agents' configurations or model settings, thereby protecting information that may be proprietary or private. These results establish PEARL as a scalable and practical framework for heterogeneous collaborative perception.
Comments21 pages, 4 figures and 25 tables
