# Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving

arXiv: https://arxiv.org/abs/2401.10153 · HTML: https://mlsanity.com/paper/2401.10153/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [Deep Learning Enabled Semantic Communication Systems](https://mlsanity.com/paper/2006.10685/) (2020)
- [6G Networks: Beyond Shannon Towards Semantic and Goal-Oriented Communications](https://mlsanity.com/paper/2011.14844/) (2020)
- [Semantic Communication Systems for Speech Transmission](https://mlsanity.com/paper/2102.12605/) (2021)
- [A Lite Distributed Semantic Communication System for Internet of Things](https://mlsanity.com/paper/2007.11095/) (2020)
- [Wireless Semantic Communications for Video Conferencing](https://mlsanity.com/paper/2204.07790/) (2022)
- [Wireless Deep Video Semantic Transmission](https://mlsanity.com/paper/2205.13129/) (2022)
- [WITT: A Wireless Image Transmission Transformer for Semantic Communications](https://mlsanity.com/paper/2211.00937/) (2022)
- [Task-Oriented Image Transmission for Scene Classification in Unmanned Aerial Systems](https://mlsanity.com/paper/2112.10948/) (2021)
- [Deep Source-Channel Coding for Sentence Semantic Transmission with HARQ](https://mlsanity.com/paper/2106.03009/) (2021)
- [Semantic Communications for Speech Signals](https://mlsanity.com/paper/2012.05369/) (2020)
- [Robust Semantic Communications Against Semantic Noise](https://mlsanity.com/paper/2202.03338/) (2022)
- [A Vision of C-V2X: Technologies, Field Testing and Challenges with Chinese Development](https://mlsanity.com/paper/2002.08736/) (2020)
- [On the Role of ViT and CNN in Semantic Communications: Analysis and Prototype Validation](https://mlsanity.com/paper/2306.02759/) (2023)
- [Image Segmentation Semantic Communication over Internet of Vehicles](https://mlsanity.com/paper/2210.05321/) (2022)
- [xURLLC-Aware Service Provisioning in Vehicular Networks: A Semantic Communication Perspective](https://mlsanity.com/paper/2302.11993/) (2023)

## Built on by (most cited first, 7 of 7)

- [Task-Driven Semantic Quantization and Imitation Learning for Goal-Oriented Communications](https://mlsanity.com/paper/2502.17842/) (2025)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks](https://mlsanity.com/paper/2509.21259/) (2025)
- [SemAgent: Semantic-Driven Agentic AI Empowered Trajectory Prediction in Vehicular Networks](https://mlsanity.com/paper/2512.00834/) (2025)
- [Foundation Model-Based Adaptive Semantic Image Transmission for Dynamic Wireless Environments](https://mlsanity.com/paper/2509.23590/) (2025)
- [Backdoor Attacks on Prompt-Driven Video Segmentation Foundation Models](https://mlsanity.com/paper/2512.22046/) (2025)
- [A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications](https://mlsanity.com/paper/2601.22202/) (2026)

## Abstract (arXiv)

This article studies the problem of image segmentation-based semantic communication in autonomous driving. In real traffic scenes, detecting the key objects (e.g., vehicles, pedestrians and obstacles) is more crucial than that of other objects to guarantee driving safety. Therefore, we propose a vehicular image segmentation-oriented semantic communication system, termed VIS-SemCom, where image segmentation features of important objects are transmitted to reduce transmission redundancy. First, to accurately extract image semantics, we develop a semantic codec based on Swin Transformer architecture, which expands the perceptual field thus improving the segmentation accuracy. Next, we propose a multi-scale semantic extraction scheme via assigning the number of Swin Transformer blocks for diverse resolution features, thus highlighting the important objects' accuracy. Furthermore, the importance-aware loss is invoked to emphasize the important objects, and an online hard sample mining (OHEM) strategy is proposed to handle small sample issues in the dataset. Experimental results demonstrate that the proposed VIS-SemCom can achieve a coding gain of nearly 6 dB with a 60% mean intersection over union (mIoU), reduce the transmitted data amount by up to 70% with a 60% mIoU, and improve the segmentation intersection over union (IoU) of important objects by 4%, compared to traditional transmission scheme.