Reason-to-Transmit: Deliberative Adaptive Communication for Cooperative Perception

• URL: http://arxiv.org/abs/2603.20308v1
• Date: Thu, 19 Mar 2026 15:54:26 GMT
• Title: Reason-to-Transmit: Deliberative Adaptive Communication for Cooperative Perception
• Abstract summary: Reason-to-Transmit (R2T) is a framework that equips each agent with a lightweight transformer-based module.<n>R2T is evaluated against nine baselines in a multi-agent bird's-eye-view perception environment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles [51.449502491030756]
  We present a multi-task semantic communication framework for satellite-assisted autonomous driving.<n>The framework is trained end-to-end to jointly optimize both tasks.<n>Results show that the proposed approach achieves significant bandwidth reduction of up to 87.23% to 98.17%.
  arXiv  Detail & Related papers  (2026-07-15T06:49:41Z)
• Send Less, Perceive More: Masked Quantized Point Cloud Communication for Loss-Tolerant Collaborative Perception [38.10779821259225]
  We introduce QPoint2Comm, a quantized point-cloud communication framework that dramatically reduces bandwidth.<n>QPoint2Comm directly communicates quantized point-cloud indices using a shared codebook.<n>We employ a masked training strategy that simulates random packet loss, allowing the model to maintain strong performance even under severe transmission failures.
  arXiv  Detail & Related papers  (2026-02-25T08:00:48Z)
• Bandwidth-Efficient Multi-Agent Communication through Information Bottleneck and Vector Quantization [2.5782420501870296]
  We present a framework that combines information bottleneck theory with vector quantization to enable selective, bandwidth-efficient communication in multi-agent environments.<n>Our approach learns to compress and discretize communication messages while preserving task-critical information through principled information-theoretic optimization.
  arXiv  Detail & Related papers  (2026-02-02T12:32:28Z)
• Communication-Efficient Multi-Modal Edge Inference via Uncertainty-Aware Distributed Learning [60.650628083185616]
  We propose a three-stage communication-aware distributed learning framework to improve training and inference efficiency.<n>In StageI, devices perform local multi-modal self-supervised learning to obtain shared and modality-specific encoders without device--server exchange.<n>StageII, distributed fine-tuning with centralized evidential fusion calibrates per-modality uncertainty and reliably aggregates features distorted by noise or channel fading.<n>StageIII, an uncertainty-guided feedback mechanism selectively requests additional features for uncertain samples, optimizing the communication--accuracy tradeoff in the distributed setting.
  arXiv  Detail & Related papers  (2026-01-21T12:38:02Z)
• JigsawComm: Joint Semantic Feature Encoding and Transmission for Communication-Efficient Cooperative Perception [7.867653563872962]
  JigsawComm is an end-to-end trained, semantic-aware, and communication-efficient CP framework.<n>It uses a regularized encoder to extract semantically-relevant and sparse features.<n>It uses a lightweight Feature Utility Estimator to predict the contribution of each agent's features to the final perception task.
  arXiv  Detail & Related papers  (2025-11-21T23:36:24Z)
• UNCAP: Uncertainty-Guided Planning Using Natural Language Communication for Cooperative Autonomous Vehicles [79.10221881250759]
  Uncertainty-Guided Natural Language Cooperative Autonomous Planning (UNCAP) is a vision-language model-based planning approach.<n>It enables CAVs to communicate via lightweight natural language messages while explicitly accounting for perception uncertainty in decision-making.<n> Experiments across diverse driving scenarios show a 63% reduction in communication bandwidth with a 31% increase in driving safety score, a 61% reduction in decision uncertainty, and a four-fold increase in collision distance margin.
  arXiv  Detail & Related papers  (2025-10-14T21:09:09Z)
• Task-Oriented Semantic Communication in Large Multimodal Models-based Vehicle Networks [55.32199894495722]
  We investigate an LMM-based vehicle AI assistant using a Large Language and Vision Assistant (LLaVA)<n>To reduce computational demands and shorten response time, we optimize LLaVA's image slicing to selectively focus on areas of utmost interest to users.<n>We construct a Visual Question Answering (VQA) dataset for traffic scenarios to evaluate effectiveness.
  arXiv  Detail & Related papers  (2025-05-05T07:18:47Z)
• CoCMT: Communication-Efficient Cross-Modal Transformer for Collaborative Perception [14.619784179608361]
  Multi-agent collaborative perception enhances each agent's capabilities by sharing sensing information to cooperatively perform robot perception tasks.<n>Existing representative collaborative perception systems transmit intermediate feature maps, which contain significant amount of non-critical information.<n>We introduce CoCMT, an object-query-based collaboration framework that maximizes communication bandwidth by selectively extracting and transmitting essential features.
  arXiv  Detail & Related papers  (2025-03-13T06:41:25Z)
• Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification [49.66272783945571]
  Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds. We propose bidirectional communication-efficient FedS based on Entity-Wise Top-K Sparsification strategy.
  arXiv  Detail & Related papers  (2024-06-19T05:26:02Z)
• Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps [24.47241495415147]
  Multi-agent collaborative perception could significantly upgrade the perception performance. It inevitably results in a fundamental trade-off between perception performance and communication bandwidth. We propose a spatial confidence map, which reflects the spatial heterogeneity of perceptual information. We propose Where2comm, a communication-efficient collaborative perception framework.
  arXiv  Detail & Related papers  (2022-09-26T16:41:18Z)
• Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth (a position paper) [92.11330289225981]
  In many real-world scenarios, communication can be expensive and the bandwidth of the multi-agent system is subject to certain constraints. Redundant messages who occupy the communication resources can block the transmission of informative messages and thus jeopardize the performance. We propose a novel multi-agent communication module, CommGIB, which effectively compresses the structure information and node information in the communication graph to deal with bandwidth-constrained settings.
  arXiv  Detail & Related papers  (2021-12-20T07:53:44Z)
• Learning to Communicate and Correct Pose Errors [75.03747122616605]
  We study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection and motion forecasting in a cooperative manner. We propose a novel neural reasoning framework that learns to communicate, to estimate potential errors, and to reach a consensus about those errors.
  arXiv  Detail & Related papers  (2020-11-10T18:19:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.