HeteroPROPMT：一种实时且隐私保护的异构协同感知框架
HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework
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中文总结 AI 辅助
HeteroPROPMT是一种实时隐私保护异构协同感知框架，通过模块化提示与轻量调优对齐异构智能体特征，在OPV2V-H等数据集上性能优于现有方法，且参数效率高、隐私性好。
中文摘要 AI 辅助
协同感知（CP）通过共享传感器数据、中间特征和检测结果提升自主系统对周围环境的感知能力。然而在实际部署中，协同车辆常使用异构传感器、感知模型、数据集和训练域，产生特征空间偏移，劣化下游融合与检测性能。现有方法通常需重新训练融合与检测组件，或引入模态特定特征解释器，这些方法对新加入智能体的扩展性差，且常需访问专有元数据，引发隐私问题。我们提出HeteroPROMPT，一种用于异构协同感知的实时且隐私保护框架。HeteroPROMPT通过模块化提示和基于轻量学习的调优，快速将每个异构智能体的特征与以自身为中心的统一特征空间对齐，同时保持智能体编码器及协同融合与检测栈冻结。其基于视觉提示的训练与推理以低计算开销调制鸟瞰图（BEV）特征的通道与空间位置。对于无元数据部署，自动编码器学习紧凑统一表示并从共享特征中提取模态线索，实现实时模态分类并路由至适当的HeteroPROMPT模块，且不暴露专有智能体信息。在OPV2V-H和V2XSet数据集上的实验表明，HeteroPROMPT相比最先进的异构CP方法提升了平均精度，同时使用的可训练参数数量少几个数量级，提供了可扩展且实用的CP解决方案。所提出的模态分类器在部署期间还能从紧凑特征中以大于99.99%的准确率预测新加入智能体的模态。代码将在该https URL提供。
英文摘要
Collaborative Perception (CP) improves autonomous systems' awareness of their surroundings by sharing sensor data, intermediate features, and detection results. In real-world deployments, however, collaborating vehicles often use heterogeneous sensors, perception models, datasets, and training domains, creating feature-space shifts that degrade downstream fusion and detection. Existing approaches typically retrain fusion and detection components or introduce modality-specific feature interpreters. These methods scale poorly to newly joining agents and often require access to proprietary metadata, raising privacy concerns. We propose HeteroPROMPT, a real-time and privacy-preserving framework for heterogeneous collaborative perception. HeteroPROMPT rapidly aligns each heterogeneous agent's features with an ego-centric unified feature space through modular prompts and lightweight learning-based tuning, while keeping agent encoders and the collaborative fusion and detection stacks frozen. Its visual prompt-based training and inference modulate Bird's Eye View (BEV) features across channels and spatial locations with low computational overhead. For metadata-free deployment, an autoencoder learns a compact unified representation and extracts modality cues from shared features, enabling real-time modality classification and routing to the appropriate HeteroPROMPT modules without exposing proprietary agent information. Experiments on the OPV2V-H and V2XSet datasets show that HeteroPROMPT improves Average Precision over state-of-the-art heterogeneous CP methods while using orders of magnitude fewer trainable parameters. This offers a scalable and practical CP solution. The proposed modality classifier also predicts the joining agent's modality from compact features with greater than 99.99 percent accuracy during deployment. Code will be available at https://github.com/arminmaleki007/HeteroPROMPT.
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。
