Large Language Models Take the Wheel in Autonomous Vehicle Fleet Scheduling

A fleet of driverless delivery vans hums through a city at dusk. Orders are arriving at random, traffic is shifting by the minute, and every idle vehicle represents wasted money while every missed order represents a lost customer. Deciding which vehicle should serve which order, and when, is one of the hardest operational puzzles in the emerging world of smart urban mobility. A new study published in Applied Intelligence argues that the missing piece may be an unexpected one: a large language model, the same class of artificial intelligence that powers conversational chatbots, now retooled to run the scheduling brain of autonomous vehicle fleets.

The research, led by Jiaxin Tan and corresponding author Xiaohui Huang of East China Jiaotong University in Nanchang, China, together with colleagues Nan Jiang, Xuebo Cheng, Liyan Xiong and Ni Liu, introduces a framework called the Predictive-context Large Language Model Scheduler, or P-LLM. The core idea is deceptively simple. Instead of relying solely on hand-crafted optimization rules or reinforcement learning agents that must be trained for weeks, the framework lets a large language model reason directly over the state of the fleet, the stream of incoming orders, and a forecast of demand that has not yet materialized. The scheduler then proposes vehicle-order assignments that balance immediate profit against the anticipated geography of future demand.

The problem the authors tackle is well known to operations researchers. Traditional scheduling methods, including classical optimization approaches, perform well when the world is static and the rules are clearly defined. Real cities are anything but static. Traffic conditions evolve continuously, order arrival patterns swing between feast and famine, and the best decision for the current moment can be a disaster five minutes later if it strands vehicles in neighborhoods where demand is about to spike. Reinforcement learning, the go-to modern alternative, learns policies through trial and error, but the authors point to persistent challenges with training stability and convergence, particularly in environments as noisy and non-stationary as urban traffic.

P-LLM addresses this by combining three cooperating modules. The first is an Order Demand Prediction Module, which mines historical order data to forecast how many orders are likely to appear in each zone of the city in upcoming time steps. The second is the LLM-Based Scheduling Module, the heart of the system, which receives structured, machine-readable descriptions of current orders, vehicle availability, estimated arrival times, and the demand forecast, and then generates candidate assignments. The third is a Scheduling Output Verification Module, a safety net that checks whether the language model’s decisions are actually reasonable and feasible before they are executed.

That verification module matters more than it might first appear. Large language models are famously fluent but not always reliable, and a scheduler that assigns the same vehicle to two orders at once, or accepts an order that no vehicle can reach within its time window, would be worse than useless. The published framework enforces hard feasibility constraints through this separate verification layer, while the language model itself is instructed to follow softer reasoning principles. According to the prompt design documented in the paper’s appendix, the model is cast as an autonomous vehicle scheduling expert for smart cities, instructed to reason about the economic value of each order using rewards and travel costs, to promote efficient use of idle vehicles, and to avoid over-allocating vehicles to regions expected to see high future demand.

One of the more intriguing design choices is what the authors call demand-aware selective rejection. The scheduler is explicitly permitted to decline orders that are economically unfavorable or clearly infeasible. Under conditions of limited vehicle availability, it may reject low-value orders while weighing the predicted future demand in the same zone, effectively deciding that a vehicle parked in a soon-to-be-busy district is worth more than the marginal profit of a cheap fare right now. This kind of judgment, blending immediate economics with spatial foresight, is precisely the sort of contextual reasoning that language models excel at, and it is difficult to encode in a fixed reward function.

The decision loop itself follows a structured workflow. At each time step, the model analyzes current orders and vehicle states, interprets the demand predictions and zone-level demand rankings, generates candidate vehicle-order assignments guided by profit, feasibility and future demand awareness, performs a self-consistency check to catch obvious violations such as repeated vehicle usage, and only then outputs its final assignment. The output must conform to a consistent structured format, which allows the downstream verification module to parse and validate every decision mechanically. In effect, the system treats the language model as a powerful but fallible reasoning engine, wrapped in guardrails that catch its mistakes before they reach the road.

The experimental results reported in the paper are striking. The proposed method outperformed both traditional scheduling methods and existing reinforcement learning baselines. More importantly for real-world deployment, performance remained stable and consistent across different real-world scenarios, time periods, fleet sizes and order volumes. That robustness addresses one of the most common criticisms of learned scheduling systems, which often excel in the specific conditions they were trained on but degrade when demand patterns shift, a fleet grows, or a new service area is added. A scheduler that generalizes across these axes is far more attractive to operators of delivery fleets and ride-hailing services, whose operating conditions change daily.

The study arrives amid a rapidly growing body of work applying large language models to transportation. Recent research has explored LLMs as decision makers for autonomous driving, as agents for adaptive traffic signal control, as generators of reward functions for highway driving, and as reasoning engines for traffic scene risk assessment. A closely related preprint, LLM-ODDR, applies language models to joint order dispatching and driver repositioning in ride-hailing. The new work extends this line into fleet scheduling with an explicit spatio-temporal demand forecast feeding the model’s context, which the authors argue is what allows the scheduler to act on the future rather than merely react to the present.

The implications reach beyond academic benchmarks. Unmanned delivery vehicles and autonomous taxis are already being deployed in smart cities, and fleet management is where their economics are won or lost. A framework that can incorporate predicted demand, respect operational constraints, and adapt to changing conditions without lengthy retraining could shorten the path from pilot projects to profitable services. The authors have released their simulator code on the Gitee platform, inviting other researchers to build on the approach. The study’s underlying datasets, which contain sensitive vehicle trajectory information, cannot be publicly shared due to privacy constraints but are available from the corresponding author upon reasonable request with data provider approval.

There are, of course, open questions. 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. The verification module, while essential, also means the language model’s suggestions are constrained rather than fully autonomous, a design that reflects a broader consensus in AI safety research: powerful generative models are best deployed where their outputs can be checked. Still, the work offers a compelling glimpse of a new architecture for urban logistics, one in which forecasting, reasoning and verification are stitched together around a language model that can read the city’s pulse and position its fleet accordingly. If the reported gains hold up in deployment, the chatbot’s cousin may soon be deciding where your delivery van goes next.

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. Applied Intelligence, 56(14), Article 423. https://doi.org/10.1007/s10489-026-07451-3

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07451-3

Keywords: large language models, autonomous vehicles, vehicle scheduling, fleet management, demand prediction, reinforcement learning, smart cities, ride-hailing, logistics optimization, LLM decision-making, order dispatching, Applied Intelligence