轻量级框架解决异构自动驾驶车辆协同感知难题
Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles
- 通过低秩视觉提示对齐异构特征，统一到共享空间
- 参数量减少94%，在OPV2V-H上检测性能提升2%
- 无需重训练大模型，兼顾效率与隐私保护
协同感知（CP）通过融合多车信息提升自动驾驶车辆的环境感知能力，但现有方法多假设车辆传感器和感知模型一致，难以应对真实场景中异构设备带来的特征域差异。传统方案需重新训练完整模型或为每类新车辆配置特征转换器，计算开销大且泄露隐私。本文提出Faster-HEAL，采用低秩视觉提示实现异构特征到统一空间的对齐，并结合金字塔融合进行鲁棒特征聚合。该方法将可训练参数减少94%，支持快速适配新车辆类型而无需重训练大模型。在OPV2V-H数据集上的实验表明，Faster-HEAL相比当前最优方法检测性能提升2%，同时计算开销显著降低，为可扩展的异构协同感知提供实用解决方案。
原文摘要 · Abstract (English)
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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