Hybrid LLM-DDQN based Joint Optimization of V2I Communication and Autonomous Driving

TL;DR

This paper proposes a hybrid approach combining large language models and double deep Q-learning to jointly optimize vehicular communication and autonomous driving, leading to improved traffic safety and network performance.

Contribution

It introduces a novel iterative optimization framework integrating LLMs with reinforcement learning for joint V2I and AD decision-making.

Findings

Outperforms conventional DDQN in convergence speed

Achieves higher average rewards in simulations

Enhances traffic safety and network efficiency

Abstract

Large language models (LLMs) have received considerable interest recently due to their outstanding reasoning and comprehension capabilities. This work explores applying LLMs to vehicular networks, aiming to jointly optimize vehicle-to-infrastructure (V2I) communications and autonomous driving (AD) policies. We deploy LLMs for AD decision-making to maximize traffic flow and avoid collisions for road safety, and a double deep Q-learning algorithm (DDQN) is used for V2I optimization to maximize the received data rate and reduce frequent handovers. In particular, for LLM-enabled AD, we employ the Euclidean distance to identify previously explored AD experiences, and then LLMs can learn from past good and bad decisions for further improvement. Then, LLM-based AD decisions will become part of states in V2I problems, and DDQN will optimize the V2I decisions accordingly. After that, the AD and V2I decisions are iteratively optimized until convergence. Such an iterative optimization approach can better explore the interactions between LLMs and conventional reinforcement learning techniques, revealing the potential of using LLMs for network optimization and management. Finally, the simulations demonstrate that our proposed hybrid LLM-DDQN approach outperforms the conventional DDQN algorithm, showing faster convergence and higher average rewards.