From Concept to Representation: Modeling Driving Capability and Task Demand with a Multimodal Large Language Model

Driving safety hinges on the dynamic interplay between task demand and driving capability, yet these concepts lack a unified, quantifiable formulation. In this work, we present a framework based on a multimodal large language model that transforms heterogeneous driving signals-scene images, maneuver descriptions, control inputs, and surrounding traffic states-into low-dimensional embeddings of task demand and driving capability. By projecting both embeddings into a shared latent space, the framework yields an interpretable measurement of task difficulty that alerts to capability shortfalls before unsafe behavior arises. Built upon a customized BLIP 2 backbone and fine-tuned on diverse simulated driving scenarios, the model respects consistency within tasks, captures impairment-related capability degradation, and can transfer to real-world motorway data without additional training. These findings endorse the framework as a concise yet effective step toward proactive, explainable risk assessment in intelligent vehicles.