Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles

TL;DR

Faster-HEAL is a lightweight, privacy-preserving collaborative perception framework that aligns heterogeneous sensor data in autonomous vehicles, significantly improving detection accuracy with minimal computational cost.

Contribution

It introduces a low-rank visual prompt and pyramid fusion to efficiently adapt to diverse vehicle sensors without retraining large models.

Findings

Achieves 2% higher detection accuracy than state-of-the-art methods.

Reduces trainable parameters by 94%, enabling efficient adaptation.

Demonstrates effectiveness on the OPV2V-H dataset.

Abstract

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.