人工知能学会

This study explores a real-time vehicle motion analysis approach by fine-tuning open-source large language models (LLMs) using numerical vehicle data. Real-time motion analysis can help provide driving feedback, develop training modules for novice drivers, and assess driving performance using signals like speed, acceleration, and steering angle. A wide range of LLMs exists, including large-scale cloud-based models (e.g., GPT-4o, Gemini-2.0) and open-source solutions (e.g., Llama-3.1). Cloud-based models provide high-quality driving feedback but are expensive, slow, and not always available for real-time use. In contrast, open-source models are more accessible and can be deployed locally, but they struggle with understanding complex numerical data. To tackle this, we fine-tuned the LLaMA 3.1 model using data gathered from the Assetto Corsa racing simulator, which captures both typical and extreme driving conditions. Our model achieved 84.07% accuracy in classifying different driving behaviors, such as smooth braking and sudden acceleration, showing that fine-tuned LLMs can effectively interpret vehicle data.