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Multimodal generative AI for automated pavement condition assessment: Benchmarking model performance.

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  • 1School of Planning, Design, and Construction, Michigan State University, East Lansing, Michigan, United States of America.

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Summary
This summary is machine-generated.

Generative artificial intelligence (GAI) models can assess road pavement conditions using street-level images. GPT-4o is recommended for its balance of speed, accuracy, and cost-effectiveness in pavement management.

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Area of Science:

  • Civil Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Road pavement condition assessment is crucial for safety and maintenance.
  • Traditional methods are costly, slow, and difficult to scale.
  • Generative AI offers automated solutions for visual interpretation using street-level imagery.

Purpose of the Study:

  • To evaluate multimodal large language models (MLLMs) for automated road surface condition assessment.
  • To compare the performance of proprietary and open-source MLLMs on pavement management tasks.
  • To identify the most effective MLLM for cost-efficient and accurate pavement analysis.

Main Methods:

  • Tested seven MLLMs (Gemini 2.5 Pro, OpenAI o1, GPT-4o, Gemma 3, Llama 3.2, LLaVA v1.6 Mistral, LLaVA v1.6 Vicuna).
  • Evaluated models on distress identification, spatial patterns, severity, and maintenance intervals.
  • Assessed performance based on response rate, correctness, consistency, multimodal errors, and cost.

Main Results:

  • MLLMs can interpret street-level imagery for pavement assessment.
  • Models generate task-relevant outputs cost-effectively.
  • GPT-4o demonstrated a strong balance of responsiveness, accuracy, and computational cost.

Conclusions:

  • MLLMs show promise for automating pavement condition assessment.
  • GPT-4o is recommended as a preferred model for its performance metrics.
  • This technology can optimize roadway maintenance investments and improve safety.