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.
arXiv:2609.30629v1 Announce Type: cross Abstract: Mission-critical UAVs increasingly rely on split vision-language model (VLM) perception under tight onboard-resource and wireless-communication const...
arXiv:2608. 10198v1 Announce Type: new Abstract: Latent-space communication allows heterogeneous vision-language model agents to exchange continuous representations without serializing visual and reasoning states into text.
arXiv:2609.00951v1 Announce Type: new Abstract: Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability o...
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.
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:2603.19308v2 Announce Type: replace-cross Abstract: In autonomous driving, multi-agent collaborative perception enhances sensing capabilities by enabling agents to share perceptual data. A key...
arXiv:2609.23974v1 Announce Type: new Abstract: Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through vis...
arXiv:2607. 18540v1 Announce Type: cross Abstract: Robotic perception pipelines increasingly rely on large vision backbones deployed on SWaP-constrained edge platforms, making post-training quantization (PTQ) attractive for real-time inference.
Collaborative perception shares information among multiple agents to obtain a comprehensive scene representation, enhancing the perceptual capability of individual agents. However, most existing metho...
The paper introduces HMS‑SCP, a hierarchical multi‑scale semantic‑aware cooperative perception framework for V2X communication. It uses a spatial importance predictor to select task‑relevant grid elements at multiple scales and maps them directly into complex‑valued symbols for joint source‑channel coding, achieving ultra‑low symbol rates and noise resilience. Experiments on OPV2V and DAIR‑V2X show that HMS‑SCP maintains high‑confidence far‑field detection with sub‑16 ms latency even under severe Rayleigh fading and extreme compression.
Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through visual perception. A representative example is Human...
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.