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From Concept To Representation: Modeling Driving Capability And Task Demand With A Multimodal Large Language Model.

Home
From Concept To Representation: Modeling Driving Capability And Task Demand With A Multimodal Large Language Model.

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

Haoran Zhou¹, Alexander Carballo^2,3,4, Keisuke Fujii^1,5

¹Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya 464-8603, Japan.

Sensors (Basel, Switzerland)

|September 27, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a multimodal large language model framework to quantify driving task demand and capability. It provides an interpretable measure of task difficulty, enabling proactive risk assessment in intelligent vehicles.

Keywords:

driving capability multimodal large language model representation learning task demand task difficulty task–capability interface model

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Area of Science:

Intelligent transportation systems
Artificial intelligence in automotive safety
Human-computer interaction in driving

Background:

Driving safety relies on the balance between task demand and driver capability.
Current methods lack a unified, quantifiable approach to assess this interplay.
Existing frameworks struggle to predict potential capability shortfalls before unsafe driving occurs.

Purpose of the Study:

To develop a novel framework for quantifying driving task demand and capability using multimodal data.
To create an interpretable measurement of task difficulty for proactive risk assessment.
To enable early detection of driver capability degradation and potential safety risks.

Main Methods:

Utilized a multimodal large language model (LLM) integrating scene images, maneuver descriptions, control inputs, and traffic states.

Transformed heterogeneous driving signals into low-dimensional embeddings for task demand and driving capability.

Projected embeddings into a shared latent space for interpretable task difficulty measurement.

Employed a customized BLIP 2 backbone, fine-tuned on diverse simulated driving scenarios.

Main Results:

The framework successfully quantifies task demand and driving capability from multimodal inputs.
An interpretable measurement of task difficulty was generated, correlating with potential capability shortfalls.
The model demonstrated consistency within tasks and captured impairment-related capability degradation.
The framework showed effective transferability to real-world motorway data without retraining.

Conclusions:

The proposed LLM-based framework offers a concise and effective method for proactive risk assessment in intelligent vehicles.
It provides an explainable measurement of driving difficulty, crucial for enhancing vehicle safety.
This approach paves the way for more sophisticated safety systems that anticipate and mitigate risks before they materialize.