Collaborative perception (CP) is a promising paradigm for improving situational awareness in autonomous vehicles by overcoming the limitations of single-agent perception. However, most existing approaches assume homogeneous agents, which restricts their applicability in real-world scenarios where vehicles use diverse sensors and perception models. This heterogeneity introduces a feature domain gap that degrades detection performance. Prior works address this issue by retraining entire models/major components, or using feature interpreters for each new agent type, which is computationally expensive, compromises privacy, and may reduce single-agent accuracy. We propose Faster-HEAL, a lightweight and privacy-preserving CP framework that fine-tunes a low-rank visual prompt to align heterogeneous features with a unified feature space while leveraging pyramid fusion for robust feature aggregation. This approach reduces the trainable parameters by 94%, enabling efficient adaptation to new agents without retraining large models. Experiments on the OPV2V-H dataset show that Faster-HEAL improves detection performance by 2% over state-of-the-art methods with significantly lower computational overhead, offering a practical solution for scalable heterogeneous CP.

翻译：协同感知是一种通过克服单智能体感知的局限性来提升自动驾驶车辆态势感知能力的前瞻性范式。然而，现有方法大多假设智能体是同质的，这限制了其在现实场景中的应用，因为现实中的车辆通常使用多样化的传感器和感知模型。这种异构性会引入特征域差异，从而降低检测性能。先前的研究通过重新训练整个模型/主要组件，或为每种新智能体类型使用特征解释器来解决此问题，但这通常计算成本高昂、损害隐私，并可能降低单智能体精度。我们提出了Faster-HEAL，一个轻量级且隐私保护的协同感知框架。该框架通过微调一个低秩视觉提示，将异构特征对齐到一个统一的特征空间，同时利用金字塔融合进行鲁棒的特征聚合。该方法将可训练参数减少了94%，从而能够高效适应新智能体，而无需重新训练大型模型。在OPV2V-H数据集上的实验表明，Faster-HEAL在显著降低计算开销的同时，将检测性能较现有先进方法提升了2%，为可扩展的异构协同感知提供了一个实用的解决方案。