# A Multi-Task Oriented Semantic Communication Framework for Autonomous Vehicles

arXiv: https://arxiv.org/abs/2403.12997 · HTML: https://mlsanity.com/paper/2403.12997/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 6 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [Density Modeling of Images using a Generalized Normalization Transformation](https://mlsanity.com/paper/1511.06281/) (2015)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities](https://mlsanity.com/paper/2012.07753/) (2020)
- [Goal-Oriented Communications for the IoT and Application to Data Compression](https://mlsanity.com/paper/2211.05378/) (2022)
- [A Learning-Based Fast Uplink Grant for Massive IoT via Support Vector Machines and Long Short-Term Memory](https://mlsanity.com/paper/2108.10070/) (2021)
- [Role of Deep Learning in Wireless Communications](https://mlsanity.com/paper/2210.02596/) (2022)
- [The Key Concepts of Ethics of Artificial Intelligence - A Keyword based Systematic Mapping Study](https://mlsanity.com/paper/1809.07027/) (2018)
- [Wireless Image Transmission with Semantic and Security Awareness](https://mlsanity.com/paper/2212.00227/) (2022)

## Built on by (most cited first, 10 of 10)

- [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)
- [World Model-Enabled Causal Digital Twins for Semantic Communications in Physical AI Systems](https://mlsanity.com/paper/2605.16547/) (2026)
- [Semantic Meta-Split Learning: A TinyML Scheme for Few-Shot Wireless Image Classification](https://mlsanity.com/paper/2409.12978/) (2024)
- [An Analysis of Minimum Error Entropy Loss Functions in Wireless Communications](https://mlsanity.com/paper/2410.07208/) (2024)
- [TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments](https://mlsanity.com/paper/2506.21900/) (2025)
- [A Survey on Deep Multi-Task Learning in Connected Autonomous Vehicles](https://mlsanity.com/paper/2508.00917/) (2025)
- [Cyber Physical Awareness via Intent-Driven Threat Assessment: Enhanced Space Networks with Intershell Links](https://mlsanity.com/paper/2508.16314/) (2025)
- [A VAE-Driven Multi-Task Satellite-Aided Semantic Communication Framework for 6G-Enabled Connected Autonomous Vehicles](https://mlsanity.com/paper/2607.13494/) (2026)
- [Task-Oriented Semantic Communication for Hazard Warning and Remote Operation in Connected Vehicle Platoons](https://mlsanity.com/paper/2609.06242/) (2026)

## Abstract (arXiv)

Task-oriented semantic communication is an emerging technology that transmits only the relevant semantics of a message instead of the whole message to achieve a specific task. It reduces latency, compresses the data, and is more robust in low SNR scenarios. This work presents a multi-task-oriented semantic communication framework for connected and autonomous vehicles (CAVs). We propose a convolutional autoencoder (CAE) that performs the semantic encoding of the road traffic signs. These encoded images are then transmitted from one CAV to another CAV through satellite in challenging weather conditions where visibility is impaired. In addition, we propose task-oriented semantic decoders for image reconstruction and classification tasks. Simulation results show that the proposed framework outperforms the conventional schemes, such as QAM-16, regarding the reconstructed image's similarity and the classification's accuracy. In addition, it can save up to 89 % of the bandwidth by sending fewer bits.