[4K2-IS-2e] Machine learning

[4K2-IS-2e-04] AI-Enabled Vehicle Motion Analysis: Fine-Tuning Open-Source LLMs for Real-Time Insights

〇Bersilin Charles Robert¹, Shotaro Nishimura¹, Ko Uchida¹, Hiroshi Honda¹ (1. Honda Motor Co., Ltd.)

Keywords:Generative AI, Fine-Tuning, Large Language Models, Driving Insights, Human Computer Interaction

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.