PEARL: A Lightweight Prompt-based Feature Interpreter Framework for Real-Time, Anonymous, and Heterogeneous Collaborative Perception

arXiv:2609.27123 · cs.CV, cs.LG · Submitted 2026-09-22 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-22

Updated: 2026-09-22

Comments: 21 pages, 4 figures and 25 tables

Code: https://github.com/arminmaleki007/PEARL

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Adaptation — 39× in this paper · explained in 1 episode
• Collaborative Perception — 27× in this paper
• Alignment — 24× in this paper · explained in 1 episode
• Cosine Similarity — 19× in this paper · explained in 3 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Ablation — 7× in this paper · explained in 1 episode
• Domain Shift — 6× in this paper · explained in 4 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Transformer — 4× in this paper · explained in 2 episodes
• Ablation study — 3× in this paper · explained in 1 episode
• Adversarial Training — 3× in this paper · explained in 1 episode
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Domain Adaptation — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Communication Overhead — 2× in this paper · explained in 2 episodes
• International Conference on Robotics and Automation — 2× in this paper
• Late Fusion — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Two-stage training — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Average Precision — 1× in this paper
• Calibration — 1× in this paper · explained in 7 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• IEEE Intelligent Vehicles Symposium — 1× in this paper
• Modality Gap — 1× in this paper · explained in 4 episodes
• Point Clouds — 1× in this paper · explained in 2 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Two-Stage Training Strategy — 1× in this paper · explained in 3 episodes

Sources

• Vehicle-to-Everything Cooperative Perception for Autonomous Driving
• Self-Localized Collaborative Perception
• An Extensible Framework for Open Heterogeneous Collaborative Perception
• CoBEVMoE: Heterogeneity-aware Feature Fusion with Dynamic Mixture-of-Experts for Collaborative Perception
• NegoCollab: A Common Representation Negotiation Approach for Heterogeneous Collaborative Perception
• Bridging the Domain Gap for Multi-Agent Perception
• Faster-HEAL: An Efficient and Privacy-Preserving Collaborative Perception Framework for Heterogeneous Autonomous Vehicles
• Prmpt2Adpt: Prompt-Based Zero-Shot Domain Adaptation for Resource-Constrained Environments
• UPRE: Zero-Shot Domain Adaptation for Object Detection via Unified Prompt and Representation Enhancement
• CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

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