SEG-JPEG: Simple Visual Semantic Communications for Remote Operation of Automated Vehicles over Unreliable Wireless Networks

• URL: http://arxiv.org/abs/2602.15258v1
• Date: Mon, 16 Feb 2026 23:28:10 GMT
• Title: SEG-JPEG: Simple Visual Semantic Communications for Remote Operation of Automated Vehicles over Unreliable Wireless Networks
• Abstract summary: This paper investigates how computer vision assisted semantic communication can be used to circumvent data loss and corruption associated with traditional image compression techniques.<n>By encoding the segmentations of detected road users into colour coded highlights within low resolution greyscale imagery, the required data rate can be reduced by 50 %.<n>Results indicate that large-scale deployment of remotely operated automated vehicles could be possible even on the often constrained public 4G/5G mobile network.
• Score: 34.17159035998805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Remote Operation is touted as being key to the rapid deployment of automated vehicles. Streaming imagery to control connected vehicles remotely currently requires a reliable, high throughput network connection, which can be limited in real-world remote operation deployments relying on public network infrastructure. This paper investigates how the application of computer vision assisted semantic communication can be used to circumvent data loss and corruption associated with traditional image compression techniques. By encoding the segmentations of detected road users into colour coded highlights within low resolution greyscale imagery, the required data rate can be reduced by 50 \% compared with conventional techniques, while maintaining visual clarity. This enables a median glass-to-glass latency of below 200ms even when the network data rate is below 500kbit/s, while clearly outlining salient road users to enhance situational awareness of the remote operator. The approach is demonstrated in an area of variable 4G mobile connectivity using an automated last-mile delivery vehicle. With this technique, the results indicate that large-scale deployment of remotely operated automated vehicles could be possible even on the often constrained public 4G/5G mobile network, providing the potential to expedite the nationwide roll-out of automated vehicles.

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)
• Semantic-Aware Multiple Access via Spatial Redundancy Exploitation for Uplink-Dominant 6G Use Cases [17.532778146939027]
  Uplink-dominant 6G use cases require efficient transmission of high-volume visual data over limited wireless resources.<n>This paper proposes a semantic-aware multiple access scheme that exploits overlapping fields of view among vehicular users to reduce redundant uplink transmissions.
  arXiv  Detail & Related papers  (2026-06-30T14:21:33Z)
• Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks [5.862522659881676]
  Real-time urban traffic surveillance is vital for Intelligent Transportation Systems (ITS) to ensure road safety, optimize traffic flow, track vehicle trajectories, and prevent collisions in smart cities.<n>We propose a semantic communication framework that significantly reduces transmission overhead.<n>This approach achieves a 99.9% reduction in data transmission size while maintaining an LLM response accuracy of 89% for reconstructed cropped images, compared to 93% accuracy with original cropped images.
  arXiv  Detail & Related papers  (2025-09-25T14:53:36Z)
• A Dynamic Transformer Network for Vehicle Detection [57.4144097001218]
  We present a dynamic Transformer network for vehicle detection (DTNet)<n>DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector.<n>To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection.
  arXiv  Detail & Related papers  (2025-06-03T11:29:35Z)
• Scalable AI Generative Content for Vehicular Network Semantic Communication [46.589349524682966]
  This paper unveils a scalable Artificial Intelligence Generated Content (AIGC) system that leverages an encoder-decoder architecture. The system converts images into textual representations and reconstructs them into quality-acceptable images, optimizing transmission for vehicular network semantic communication. Experimental results suggest that the proposed method surpasses the baseline in perceiving vehicles in blind spots and effectively compresses communication data.
  arXiv  Detail & Related papers  (2023-11-23T02:57:04Z)
• Agile gesture recognition for capacitive sensing devices: adapting on-the-job [55.40855017016652]
  We demonstrate a hand gesture recognition system that uses signals from capacitive sensors embedded into the etee hand controller. The controller generates real-time signals from each of the wearer five fingers. We use a machine learning technique to analyse the time series signals and identify three features that can represent 5 fingers within 500 ms.
  arXiv  Detail & Related papers  (2023-05-12T17:24:02Z)
• Mixed Traffic Control and Coordination from Pixels [18.37701232116777]
  Previous methods for traffic control have proven futile in alleviating current congestion levels. This gives rise to mixed traffic control, where robot vehicles regulate human-driven vehicles through reinforcement learning (RL) In this work, we show robot vehicles using image observations can achieve competitive performance to using precise information on environments.
  arXiv  Detail & Related papers  (2023-02-17T22:40:07Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Artificial Intelligence Enabled Traffic Monitoring System [3.085453921856008]
  This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs.
  arXiv  Detail & Related papers  (2020-10-02T22:28:02Z)
• Multi-Agent Routing Value Iteration Network [88.38796921838203]
  We propose a graph neural network based model that is able to perform multi-agent routing based on learned value in a sparsely connected graph. We show that our model trained with only two agents on graphs with a maximum of 25 nodes can easily generalize to situations with more agents and/or nodes.
  arXiv  Detail & Related papers  (2020-07-09T22:16:45Z)
• Deep Learning Assisted CSI Estimation for Joint URLLC and eMBB Resource Allocation [36.364156900974535]
  We propose a deep learning assisted CSI estimation technique in highly mobile vehicular networks. We formulate and solve a dynamic network slicing based resource allocation problem for vehicular user equipments.
  arXiv  Detail & Related papers  (2020-03-12T10:00:08Z)

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.