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

• URL: http://arxiv.org/abs/2603.07314v1
• Date: Sat, 07 Mar 2026 19:18:25 GMT
• Title: Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles
• Abstract summary: Collaborative perception is a promising paradigm for improving situational awareness in autonomous vehicles.<n>We propose Faster-HEAL, a framework that fine-tunes a low-rank visual prompt to align heterogeneous features with a unified feature space.<n>This approach reduces the trainable parameters by 94%, enabling efficient adaptation to new agents without retraining large models.
• Score: 4.904389322133549
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• HeteroPROPMT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework [4.904389322133549]
  Collaborative Perception (CP) improves autonomous systems' awareness of their surroundings by sharing sensor data, intermediate features, and detection results.<n>Existing approaches typically retrain fusion and detection components or introduce modality-specific feature interpreters.<n>We propose HeteroPROMPT, a real-time and privacy-preserving framework for heterogeneous collaborative perception.
  arXiv  Detail & Related papers  (2026-07-28T21:24:43Z)
• FedRio: Personalized Federated Social Bot Detection via Cooperative Reinforced Contrastive Adversarial Distillation [52.56850762009136]
  Social bot detection is critical to the stability and security of online social platforms.<n>Current state-of-the-art bot detection models are largely developed in isolation.<n>We propose FedRio as a cross-platform and cross-model detection framework.
  arXiv  Detail & Related papers  (2026-04-12T15:13:41Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• HeatV2X: Scalable Heterogeneous Collaborative Perception via Efficient Alignment and Interaction [7.171380055232685]
  Vehicle-to-Everything (V2X) collaborative perception extends sensing beyond single vehicle limits through transmission.<n>Existing frameworks face two key challenges: (1) the participating agents are inherently multi-modal and heterogeneous, and (2) the collaborative framework must be scalable to accommodate new agents.<n>We propose Heterogeneous Adaptation (HeatV2X), a scalable collaborative framework.
  arXiv  Detail & Related papers  (2025-11-13T11:33:22Z)
• Pragmatic Heterogeneous Collaborative Perception via Generative Communication Mechanism [14.40993352402385]
  We present a novel Generative Communication mechanism (GenComm) that facilitates seamless perception across heterogeneous multi-agent systems.<n>Experiments conducted on the OPV2V-H, DAIR-V2X and V2X-Real datasets demonstrate that GenComm outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-10-22T14:15:20Z)
• Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection [108.5042835056188]
  This work introduces Agent4FaceForgery to address two fundamental problems.<n>How to capture the diverse intents and iterative processes of human forgery creation.<n>How to model the complex, often adversarial, text-image interactions that accompany forgeries in social media.
  arXiv  Detail & Related papers  (2025-09-16T01:05:01Z)
• ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding [71.654781631463]
  ReAgent-V is a novel agentic video understanding framework.<n>It integrates efficient frame selection with real-time reward generation during inference.<n>Extensive experiments on 12 datasets demonstrate significant gains in generalization and reasoning.
  arXiv  Detail & Related papers  (2025-06-02T04:23:21Z)
• STAMP: Scalable Task And Model-agnostic Collaborative Perception [24.890993164334766]
  STAMP is a task- and model-agnostic, collaborative perception pipeline for heterogeneous agents.<n>It minimizes computational overhead, enhances scalability, and preserves model security.<n>As a first-of-its-kind framework, STAMP aims to advance research in scalable and secure mobility systems towards Level 5 autonomy.
  arXiv  Detail & Related papers  (2025-01-24T16:27:28Z)
• An Extensible Framework for Open Heterogeneous Collaborative Perception [58.70875361688463]
  Collaborative perception aims to mitigate the limitations of single-agent perception. In this paper, we introduce a new open heterogeneous problem: how to accommodate continually emerging new heterogeneous agent types into collaborative perception. We propose HEterogeneous ALliance (HEAL), a novel collaborative perception framework.
  arXiv  Detail & Related papers  (2024-01-25T05:55:03Z)
• MACP: Efficient Model Adaptation for Cooperative Perception [23.308578463976804]
  We propose a new framework termed MACP, which equips a single-agent pre-trained model with cooperation capabilities. We demonstrate in experiments that the proposed framework can effectively utilize cooperative observations and outperform other state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-10-25T14:24:42Z)
• An automatic differentiation system for the age of differential privacy [65.35244647521989]
  Tritium is an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML) We introduce Tritium, an automatic differentiation-based sensitivity analysis framework for differentially private (DP) machine learning (ML)
  arXiv  Detail & Related papers  (2021-09-22T08:07:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.