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.
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.
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.
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.
MC-DeTra is a reimplementation of the DeTra model that jointly performs object detection and socially-aware trajectory forecasting in bird's-eye-view images. It introduces motion-consistency mechanisms that add supervision from each actor’s past motion, surrounding traffic occupancy, and a consistency constraint aligning predicted heading with motion direction. The added losses are train‑only and inference‑safe, improving dynamic trajectory forecasting on the Waymo Open Dataset while maintaining or enhancing detection accuracy.
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.
FRUC is a feedforward 3D Gaussian Splatting framework that reconstructs dynamic scenes from uncalibrated collaborative driving views. It uses a visual‑grounded geometric Transformer backbone for one‑shot, calibration‑free inference and introduces an ego‑centric causal occlusion field to model occlusion evolution across agents. The method performs cross‑agent integration as a deterministic residual denoising process, achieving state‑of‑the‑art rendering quality and efficiency on V2X‑Real and UrbanIng‑V2X datasets.
The paper introduces Co‑VLN, a minimalist, model‑agnostic framework that lets agents in Vision‑Language Navigation share structured perceptual memory when they independently visit the same locations. By exchanging observations, each agent expands its receptive field without extra exploration. Experiments on the R2R benchmark with DUET and MapGPT show that vision‑sharing significantly improves performance, suggesting a promising direction for collaborative embodied navigation.
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...
OpenSAL360 is an open‑source platform that enables scalable, low‑cost collection of 360° video saliency data using only a standard screen, mouse, and internet connection. It bypasses the need for VR headsets, allowing parallel data collection from crowdsourced assessors. The authors validated the protocol against seven VR eye‑tracking datasets, performed ablation studies, and released a new dataset of 500 omnidirectional videos annotated by over 2,000 assessors, the largest in the field to date.
arXiv:2606. 07708v1 Announce Type: cross Abstract: We introduce a dataset and benchmark for cross-view urban traffic perception built from synchronized ego-centric bicycle videos and aerial drone videos recorded at real urban intersections.