Can LLMs improve autonomous vehicle intersection safety?
A Vehicle-Infrastructure Multi-layer Cooperative Decision-making Framework
This paper proposes a multi-layered framework for coordinating autonomous vehicles (CAVs) at intersections using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. It aims to improve safety and efficiency beyond the capabilities of individual vehicle algorithms.
Crucially, the framework utilizes Large Language Models (LLMs) to negotiate the passing order of vehicles. LLMs manage both intra-group (within clusters of influencing vehicles) and inter-group (between clusters) negotiations to establish a safe and efficient global traffic flow. This approach mimics human-like decision-making by allowing "conversations" between vehicles (mediated by the LLM) to resolve conflicts and establish priorities. The system quantifies influence between vehicles based on factors like proximity and speed to cluster vehicles for negotiation and simplifies the computational complexity of coordination.