Semantic Communications for Vehicle-Based Mission-Critical Services: Challenges and Solutions

TL;DR

The paper addresses the need for reliable, low-latency communication in vehicle-based mission-critical services by introducing a semantic communication framework tailored to VbMC. It combines LLM-based knowledge-base maintenance, attention-based multi-modal fusion, swarm intelligence for decentralized encoder updates, XAI-guided feature filtering, and security-focused components such as covert and poisoning-security mechanisms. The authors classify typical VbMC services under SemCom, analyze challenges such as KB synchronization, multi-modal coupling, scalability, and explainability, and validate the framework with a UAV-based case study showing substantial data reduction and interpretable decisions. This work offers a practical, AI-enabled path to robust, explainable semantic networking for UAVs and IoV systems in future 6G networks.

Abstract

As mission-critical (MC) services such as Unmanned Aerial Vehicles (UAVs) based emergency communication and Internet of Vehicles (IoVs) enabled autonomous driving emerge, the traditional communication framework can not meet the growing demands for higher reliability and lower latency and the increasing transmission loads. Semantic Communication (SemCom), an emerging communication paradigm that shifts the focus from bit-level data to its context and intended task at the receiver (i.e., semantic level), is envisioned to be a key revolution in Sixth Generation (6G) networks. However, an explicit and systematic SemCom framework specifically tailored for Vehicle-based MC (VbMC) services has yet to be proposed, primarily due to the complexity and lack of analysis on their MC characteristics. In this article, we first present the key information-critical and infrastructure-critical vehicle-based services within the SemCom framework. We then analyze the unique characteristics of MC services and the corresponding challenges they present for SemCom. Building on this, we propose a novel SemCom framework designed to address the specific needs of MC services in vehicle systems, offering potential solutions to existing challenges. Finally, we present a case study on UAV-based rapid congestion relief, utilizing eXplainable AI (XAI) to validate the effectiveness of the proposed SemCom framework.

Figures (4)

• Figure 1: Challenges of vehicle-based services under generic SemCom framework.
• Figure 2: Swarm Intelligence Enabled LLM-based Semantic Communication Framework
• Figure 3: Proposed explainability and security solutions.
• Figure 4: SHAP saliency maps during one full traffic signal phase switching.