Faster-HEAL: 一种高效且隐私保护的异构自动驾驶车辆协作感知框架
Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles
AI总结：
Faster-HEAL通过低秩视觉提示和金字塔融合，实现高效且隐私保护的异构自动驾驶车辆协作感知
摘要：
协作感知（CP）是一种通过克服单体感知局限性来提高自动驾驶车辆情境感知能力的有前景范式。然而，大多数现有方法假设同质代理，这限制了其在现实世界中的应用，因为车辆使用多样化的传感器和感知模型。这种异质性引入了特征域间隙，从而降低检测性能。先前的工作通过重新训练整个模型或主要组件，或为每个新代理类型使用特征解释器来解决这个问题，这计算成本高，影响隐私，并可能降低单体准确性。我们提出了Faster-HEAL，一种轻量且隐私保护的CP框架，通过微调低秩视觉提示，将异构特征对齐到统一的特征空间，同时利用金字塔融合进行稳健的特征聚合。这种方法将可训练参数减少了94%，使能够高效适应新代理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在比现有最先进方法提高2%的检测性能的同时，具有显著更低的计算开销，提供了一种可扩展的异构CP的实用解决方案。
英文摘要：
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.
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