# Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles Source: [Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles]() Collection index: [llms.txt]() Data updated: 2026-03-11T05:00:04.181Z Authors: Armin Maleki, Hayder Radha Published: 2026-03-07T19:18:25.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on OPV2V +m2 agent H (test)](): 3D Object Detection; OPV2V +m2 agent H (test); AP@0.5: 89.3. - [3D Object Detection on OPV2V +m3 agent H (test)](): 3D Object Detection; OPV2V +m3 agent H (test); AP@0.5: 93. - [3D Object Detection on OPV2V +m4 agent H (test)](): 3D Object Detection; OPV2V +m4 agent H (test); AP@0.5: 92.7. ## Original sources and implementation [Original paper]() [PDF]()