Large Language Models Take the Wheel in Autonomous Vehicle Fleet Scheduling

The published framework enforces hard feasibility constraints through this separate verification layer, while the language model itself is instructed to follow softer reasoning principles.

The study arrives amid a rapidly growing body of work applying large language models to transportation.

Large language models are computationally expensive, and the paper does not claim to have resolved every challenge of latency and cost at massive fleet scale.

Subject of Research: Large language model-based scheduling of autonomous vehicle fleets in smart cities Article Title: A spatio-temporal context-aware LLM-centric framework for autonomous vehicle scheduling Article References: Tan, J., Huang, X., Jiang, N., Cheng, X., Xiong, L., & Liu, N. (2026).

A spatio-temporal context-aware LLM-centric framework for autonomous vehicle scheduling.