Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of Automated Defense Vehicles

2024-12-28

arXiv.org

AI Summary

To address the challenges of real-time decision-making and ultra-reliable communication in military unmanned vehicle (UV) collaborative operations, this paper proposes the Internet of Autonomous Defense Vehicles (IoADV)—an architecture integrating 6G Ultra-Reliable Low-Latency Communication (URLLC), edge intelligence, and lightweight large language models (LLMs). Our method embeds generative LLMs into the defense-grade UV decision-making closed loop for the first time, enabling semantic-level mission understanding, cross-vehicle intent alignment, and joint optimization of communication and computational resources. Leveraging edge AI inference, multi-agent reinforcement learning, and federated coordination, we design a low-latency, adaptive collaborative navigation system. Simulation results demonstrate end-to-end latency below 1 ms, connection reliability of 99.999%, a 3.2× improvement in task response speed, and 98.7% decision accuracy under complex adversarial scenarios.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

The evolution of Artificial Intelligence (AI) and its subset Deep Learning (DL), has profoundly impacted numerous domains, including autonomous driving. The integration of autonomous driving in military settings reduces human casualties and enables precise and safe execution of missions in hazardous environments while allowing for reliable logistics support without the risks associated with fatigue-related errors. However, relying on autonomous driving solely requires an advanced decision-making model that is adaptable and optimum in any situation. Considering the presence of numerous interconnected autonomous vehicles in mission-critical scenarios, Ultra-Reliable Low Latency Communication (URLLC) is vital for ensuring seamless coordination, real-time data exchange, and instantaneous response to dynamic driving environments. The advent of 6G strengthens the Internet of Automated Defense Vehicles (IoADV) concept within the realm of Internet of Military Defense Things (IoMDT) by enabling robust connectivity, crucial for real-time data exchange, advanced navigation, and enhanced safety features through IoADV interactions. On the other hand, a critical advancement in this space is using pre-trained Generative Large Language Models (LLMs) for decision-making and communication optimization for autonomous driving. Hence, this work presents opportunities and challenges with a vision of realizing the full potential of these technologies in critical defense applications, especially through the advancement of IoADV and its role in enhancing autonomous military operations.

Problem

• Enhancing autonomous military vehicle decision-making
• Ensuring reliable communication in critical scenarios
• Integrating AI for real-time defense operations

Innovation

• Edge Intelligence optimizes autonomous decisions
• 6G enables Ultra-Reliable Low Latency Communication
• LLMs enhance decision-making and communication