arXiv Computer Vision
Sep 11

HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework

HeteroPROMPT is a real‑time, privacy‑preserving framework for heterogeneous collaborative perception in autonomous systems. It aligns features from diverse sensors and models into a unified ego‑centric space using modular prompts and lightweight tuning, while keeping encoders and fusion stacks frozen. The system employs a metadata‑free autoencoder for modality classification and routing, achieving higher average precision on OPV2V‑H and V2XSet datasets with far fewer trainable parameters.

By Armin Maleki, Hayder Radha
arXiv Machine Learning
Sep 24

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

PEARL is a lightweight, prompt‑embedding framework designed for real‑time, anonymous, and heterogeneous collaborative perception. It uses two parallel, low‑rank visual prompt interpreters—sparse‑detection (LWSD) and dense, domain‑invariant (LWDDI)—to align features and select the appropriate interpreter for newly joining agents without needing their configurations. Experiments on simulated and real datasets show that PEARL improves average precision by 8.2% over random selection, runs in 1.67 ms, and reduces communication cost by up to 34.7× while outperforming state‑of‑the‑art offline methods by 5.6% AP.

By Armin Maleki, Hayder Radha
Hugging Face Trending Papers
Aug 10

Towards Collaborative Joint Perception and Prediction: Framework, Baseline Evaluation, and Deployment Perspectives

Connected Autonomous Vehicles (CAVs) increasingly exploit Vehicle-to-Everything (V2X) communication to exchange multi-source sensor information, enabling advanced Collaborative Perception (CP) capabilities. Extending beyond these capabilities, this work focuses on Collaborative Joint Perception and Prediction (Co-P&P), a paradigm that unifies CP with motion prediction to mitigate two persistent challenges: the accumulation of perception errors and visual occlusions.

arXiv Machine Learning
Sep 17

Learning from Distributed Eyes: Leveraging Collaborative Perception for Automated Model Adaptation

The paper introduces LDE, a framework that uses collaborative perception (CP) to generate high‑quality pseudo‑labels for unsupervised model adaptation in autonomous driving. It tackles communication limits, field‑of‑view mismatches, and label unreliability through selective feature sharing, FoV filtering, and curriculum learning. Experiments on 3D object detection show LDE surpasses pre‑trained models and existing adaptation methods.

By Yanan Ma, Yihang Tao, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang
arXiv Computer Vision
Sep 25

Dense Coverage, Sparse Refinement: Byte-Constrained Cooperative Perception

The paper introduces a byte‑constrained cooperative perception framework that balances dense coverage with sparse refinement. Each vehicle sends a highly compressed coarse Bird’s‑Eye‑View (BEV) layer covering the entire map and uses the remaining bandwidth to transmit high‑resolution patches selected by a Task‑Aware Benefit Selector. Experiments on DAIR‑V2X and OPV2V demonstrate that this coverage‑refinement strategy achieves superior accuracy‑payload trade‑offs, reaching 0.60 AP@0.7 with only 1.87 KB per non‑ego agent.

By Melih Yazgan, Timon M\"uller, J. Marius Z\"ollner