Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey

• URL: http://arxiv.org/abs/2505.00747v1
• Date: Wed, 30 Apr 2025 12:23:57 GMT
• Title: Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey
• Authors: Zhiying Song, Tenghui Xie, Fuxi Wen, Jun Li,
• Abstract summary: Cooperative perception extends the perception capabilities of autonomous vehicles by enabling multi-agent information sharing via Vehicle-to-Everything (V2X) communication.<n>This survey focuses on three key dimensions: information representation, information fusion, and large-scale deployment.
• Score: 4.570256768472447
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cooperative perception extends the perception capabilities of autonomous vehicles by enabling multi-agent information sharing via Vehicle-to-Everything (V2X) communication. Unlike traditional onboard sensors, V2X acts as a dynamic "information sensor" characterized by limited communication, heterogeneity, mobility, and scalability. This survey provides a comprehensive review of recent advancements from the perspective of information-centric cooperative perception, focusing on three key dimensions: information representation, information fusion, and large-scale deployment. We categorize information representation into data-level, feature-level, and object-level schemes, and highlight emerging methods for reducing data volume and compressing messages under communication constraints. In information fusion, we explore techniques under both ideal and non-ideal conditions, including those addressing heterogeneity, localization errors, latency, and packet loss. Finally, we summarize system-level approaches to support scalability in dense traffic scenarios. Compared with existing surveys, this paper introduces a new perspective by treating V2X communication as an information sensor and emphasizing the challenges of deploying cooperative perception in real-world intelligent transportation systems.

Related papers

• Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition [57.698383942708]
  Vehicle-to-everything (V2X) communication has emerged as a key enabler for extending perception range and enhancing driving safety.<n>We organized the End-to-End Autonomous Driving through V2X Cooperation Challenge, which features two tracks: cooperative temporal perception and cooperative end-to-end planning.<n>This paper describes the design and outcomes of the challenge, highlights key research problems including bandwidth-aware fusion, robust multi-agent planning, and heterogeneous sensor integration.
  arXiv  Detail & Related papers  (2025-07-29T09:06:40Z)
• Prototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification [56.065032039986725]
  Multi-source remote sensing data joint classification aims to provide accuracy and reliability of land cover classification.<n>Existing methods confront two challenges: inter-frequency multi-source feature coupling and inconsistency of complementary information exploration.<n>We present a Prototype-based Information Compensation Network (PICNet) for land cover classification based on HSI and SAR/LiDAR data.
  arXiv  Detail & Related papers  (2025-05-06T22:30:23Z)
• Is Intermediate Fusion All You Need for UAV-based Collaborative Perception? [1.8689461238197957]
  We propose a novel communication-efficient collaborative perception framework based on late-intermediate fusion, dubbed LIF.<n>We leverage vision-guided positional embedding (VPE) and box-based virtual augmented feature (BoBEV) to effectively integrate complementary information from various agents.<n> Experimental results demonstrate that our LIF achieves superior performance with minimal communication bandwidth, proving its effectiveness and practicality.
  arXiv  Detail & Related papers  (2025-04-30T16:22:14Z)
• 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)
• Semantic Communication for Cooperative Perception using HARQ [51.148203799109304]
  We leverage an importance map to distill critical semantic information, introducing a cooperative perception semantic communication framework. To counter the challenges posed by time-varying multipath fading, our approach incorporates the use of frequency-division multiplexing (OFDM) along with channel estimation and equalization strategies. We introduce a novel semantic error detection method that is integrated with our semantic communication framework in the spirit of hybrid automatic repeated request (HARQ)
  arXiv  Detail & Related papers  (2024-08-29T08:53:26Z)
• Communication-Efficient Collaborative Perception via Information Filling with Codebook [48.087934650038044]
  Collaborative perception empowers each agent to improve its perceptual ability through the exchange of perceptual messages with other agents. To address this bottleneck issue, our core idea is to optimize the collaborative messages from two key aspects: representation and selection. By integrating these two designs, we propose CodeFilling, a novel communication-efficient collaborative perception system.
  arXiv  Detail & Related papers  (2024-05-08T11:12:37Z)
• Pragmatic Communication in Multi-Agent Collaborative Perception [80.14322755297788]
  Collaborative perception results in a trade-off between perception ability and communication costs. We propose PragComm, a multi-agent collaborative perception system with two key components. PragComm consistently outperforms previous methods with more than 32.7K times lower communication volume.
  arXiv  Detail & Related papers  (2024-01-23T11:58:08Z)
• NLOS Dies Twice: Challenges and Solutions of V2X for Cooperative Perception [7.819255257787961]
  We introduce an abstract perception matrix matching method for quick sensor fusion matching procedures and mobility-height hybrid relay determination procedures. To demonstrate the effectiveness of our solution, we design a new simulation framework to consider autonomous driving, sensor fusion and V2X communication in general.
  arXiv  Detail & Related papers  (2023-07-13T08:33:02Z)
• Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving [49.42873226593071]
  We propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP) for V2X communication-aided autonomous driving. We use historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception.
  arXiv  Detail & Related papers  (2023-04-24T04:59:13Z)
• 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)
• Edge-Aided Sensor Data Sharing in Vehicular Communication Networks [8.67588704947974]
  We consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. We show that the perception accuracy is on average improved by around 80 % with only 12 kbps uplink and 28 kbps downlink bandwidth.
  arXiv  Detail & Related papers  (2022-06-17T16:30:56Z)
• V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer [58.71845618090022]
  We build a holistic attention model, namely V2X-ViT, to fuse information across on-road agents. V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention. To validate our approach, we create a large-scale V2X perception dataset.
  arXiv  Detail & Related papers  (2022-03-20T20:18:25Z)
• 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.