How can LLMs control multi-lane convoys?
ConvoyLLM: Dynamic Multi-Lane Convoy Control Using LLMs
This paper introduces ConvoyLLM, a system using Large Language Models (LLMs) to control multi-lane vehicle convoys on highways. Each vehicle uses its own LLM to make real-time driving decisions (lane changes, speed adjustments) based on its perception of the environment and the state of other vehicles in the convoy. A shared memory and interlaced formation control strategy promote cooperation and flexibility within the convoy. The system aims to improve traffic flow, safety, and fuel efficiency by intelligently coordinating autonomous vehicles in dynamic multi-lane scenarios. Key LLM aspects include per-vehicle decision-making using LLMs, few-shot prompting, a shared memory for collaborative learning, and an action decoder to translate LLM outputs into control commands.