Collaborative perception is vital for autonomous driving yet remains constrained by tight communication budgets. Earlier work reduced bandwidth by compressing full feature maps with fixed-rate encoders, which adapts poorly to a changing environment, and it further evolved into spatial selection methods that improve efficiency by focusing on salient regions, but this object-centric approach often sacrifices global context, weakening holistic scene understanding. To overcome these limitations, we introduce WhisperNet, a bandwidth-aware framework that proposes a novel, receiver-centric paradigm for global coordination across agents. Senders generate lightweight saliency metadata, while the receiver formulates a global request plan that dynamically budgets feature contributions across agents and features, retrieving only the most informative features. A collaborative feature routing module then aligns related messages before fusion to ensure structural consistency. Extensive experiments show that WhisperNet achieves state-of-the-art performance, improving [email protected] on OPV2V by 2.4% with only 0.5% of the communication cost. As a plug-and-play component, it boosts strong baselines with merely 5% of full bandwidth while maintaining robustness under localization noise. These results demonstrate that globally-coordinated allocation across what and where to share is the key to achieving efficient collaborative perception.
Figures & tables
Figure 1: Visualization of different channel groups. The proportional table in the figure represents the model’s primary (Orange), secondary (Yellow), and marginal (Green) channels.
Figure 2: Impact of Channel Pruning on Performance.
Figure 3: The architecture of WhisperNet. Each sender agent first extracts features from its raw sensor data and generates compact importance maps that summarize both spatial and channel-wise saliency. These maps are then exchanged with a receiver agent, which dynamically requests only the most critical feature subsets. Finally, the requested features are fused by a collaborative routing module.
Figure 4: Sender-Side Importance Estimation module. It evaluates feature from both spatial and channel perspectives to generate importance maps that guides communication coordination.
Figure 5: Collaborative Feature Routing module. It enables cross-vehicle feature exchange by grouping and processing semantically similar channels.
Figure 6: Plug-and-play performance validation. The figure compares the 3D object detection and BEV semantic segmentation performance when using our method (WhisperNet) and Where2comm as communication plug-ins under varying communication rates.
Figure 7: Comparison of performance and bandwidth.
Figure 8: Comparison of model robustness to mixed noise. WhisperNet shows strong resilience compared to baselines, maintaining accuracy under high pose and heading perturbations
Figure 9: Visualization of mainstream methods in challenging driving scenarios.
Communication-efficient collaborative perception studies how networked agents share useful sensor information with minimal bandwidth while improving their joint view of the scene.
Collaborative-perception enables multi-robot systems to enhance situational awareness by sharing perceptual information. Existing collaborative-perception systems face an inherent trade-off between communication bandwidth requirements and perception accuracy, where methods that exchange more information achieve better perception results at the cost of increased communication overhead. However, real-world communication networks impose bandwidth constraints that require minimizing communication overhead without sacrificing perception performance. To address this challenge, we propose HydraCollab, an adaptive collaborative-perception framework that (i) selectively transmits the most informative sensor features and (ii) dynamically employs collaboration strategies (intermediate or late) based on spatial confidence maps. Extensive evaluations on the V2X-R, V2X-Radar and UAV3D-mini datasets demonstrate that HydraCollab achieves the best overall trade-off between accuracy and communication cost among existing collaborative-perception methods. Relative to SOTA Where2comm, HydraCollab uses only 41% of the bandwidth on V2X-R and 26% on V2X-Radar while improving performance by 0.78% and 0.75% respectively. Our code and models are available at https://github.com/AICPS/HydraCollab.
Luke Chen, Cheng-Ju Wu, David R. Martin +3
Department of Electrical Engineering and Computer Science, University of California, Irvine, USA.
In multi-agent collaborative sensing systems, substantial communication overhead from information exchange significantly limits scalability and real-time performance, especially in bandwidth-constrained environments. This often results in degraded performance and reduced reliability. To address this challenge, we propose WaveComm, a wavelet-based communication framework that drastically reduces transmission loads while preserving sensing performance in low-bandwidth scenarios. The core innovation of WaveComm lies in decomposing feature maps using Discrete Wavelet Transform (DWT), transmitting only compact low-frequency components to minimize communication overhead. High-frequency details are omitted, and their effects are reconstructed at the receiver side using a lightweight generator. A Multi-Scale Distillation (MSD) Loss is employed to optimize the reconstruction quality across pixel, structural, semantic, and distributional levels. Experiments on the OPV2V and DAIR-V2X datasets for LiDAR-based and camera-based perception tasks demonstrate that WaveComm maintains state-of-the-art performance even when the communication volume is reduced to 86.3% and 87.0% of the original, respectively. Compared to existing approaches, WaveComm achieves competitive improvements in both communication efficiency and perception accuracy. Ablation studies further validate the effectiveness of its key components.
Erdemt Bao, Jin Yang
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China. · National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Shaanxi 710049, China.
Cooperative perception among autonomous agents overcomes the limitations of single-agent sensing, but bandwidth constraints in vehicle-to-everything (V2X) networks require efficient communication policies. Existing approaches rely on reactive mechanisms, such as confidence maps, learned gating, or sparse masks, to decide what to transmit, without reasoning about why a message benefits the receiver. We introduce Reason-to-Transmit (R2T), a framework that equips each agent with a lightweight transformer-based module that reasons over local scene context, estimated neighbor information gaps, and bandwidth budget to make per-region transmission decisions. Trained end-to-end with a bandwidth-aware objective, R2T is evaluated against nine baselines in a multi-agent bird's-eye-view perception environment. Any communication improves performance by about 58% AP over no communication. At low bandwidth, all selective methods perform similarly, but R2T shows clear gains under high occlusion, where information asymmetry is greatest, approaching oracle performance. All methods degrade gracefully under packet drops up to 50%, showing robustness to communication failures. These results indicate that while fusion design dominates performance, deliberative communication provides additional gains in challenging scenarios. R2T introduces a reasoning-based approach to communication, enabling more efficient and context-aware information sharing in cooperative perception.