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

Armin Maleki, Hayder Radha

TL;DR

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, is proposed, offering a practical solution for scalable heterogeneous CP.

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.

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

TL;DR

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, is proposed, offering a practical solution for scalable heterogeneous CP.

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.
Paper Structure (15 sections, 5 equations, 1 figure, 4 tables)

This paper contains 15 sections, 5 equations, 1 figure, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Collaborative Perception
  4. Heterogeneous Collaborative Perception
  5. Visual Prompts
  6. Methodology
  7. Homogeneous Base Training
  8. New heterogeneous agent training
  9. Inference Implementation
  10. Experiments
  11. Datasets
  12. Scenario Design
  13. Results
  14. Ablation Study
  15. Conclusion

Figures (1)

  • Figure 1: Overview of Faster-HEAL. Stage 1: Homogeneous base training constructs a unified feature space using pyramid fusion and trains the detection head on collaborative data from homogeneous agents. stage 2: For each new heterogeneous agent type, we freeze the pretrained encoder, pyramid fusion extractor/upsampling path, and detection head, and train a lightweight feature aligner, LIFT (visual prompts) to map its intermediate features into the unified space, and the foreground estimator. This design ensures low training cost, preserves the privacy of participating agents by requiring only their intermediate features, and enables scalable heterogeneous collaboration. In our experiments, LiDAR is used in stage 1, while camera or LiDAR could be used in stage 2.