arXiv AI
GT-Space: Enhancing Heterogeneous Collaborative Perception with Ground Truth Feature Space
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arXiv Machine Learning
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SimBEV2X: A Large-Scale Dataset and Data Generation Tool for Multi-Task Vehicle-to-Everything Cooperative Perception
arXiv:2607. 23910v1 Announce Type: cross Abstract: Cooperative perception through vehicle-to-everything (V2X) communication can overcome the inherent physical limitations of individual autonomous vehicles, such as occlusions and limited sensor range.
arXiv Computer Vision
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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.
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.
arXiv AI
Aug 11
OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving
arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.
arXiv Computer Vision
Oct 2
FedCKA: Representation-Guided Layer Personalization for Federated 3D Perception Across Driving Domains
FedCKA introduces a Centered Kernel Alignment (CKA)-based method for federated 3D perception that dynamically balances personalization and globalization. By computing layer-wise feature similarities between local client models and a global consensus model, FedCKA generates client‑specific aggregation masks to selectively share representation‑consistent layers. Experiments on a unified multi‑domain nuScenes benchmark demonstrate that FedCKA surpasses established federated baselines, improving average NDS by 7 percentage points.
arXiv Computer Vision
Sep 17
Investigating Adversarial Robustness of Heterogeneous Cooperative Perception
arXiv:2609.17856v1 Announce Type: new Abstract: Heterogeneous cooperative perception (CP) enables connected vehicles with diverse sensor setups to share spatial awareness via compact feature maps, wh...