Understanding Driving Risks using Large Language Models: Toward Elderly Driver Assessment
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
This study investigates the potential of a multimodal large language model
(LLM), specifically ChatGPT-4o, to perform human-like interpretations of
traffic scenes using static dashcam images. Herein, we focus on three judgment
tasks relevant to elderly driver assessments: evaluating traffic density,
assessing intersection visibility, and recognizing stop signs recognition.
These tasks require contextual reasoning rather than simple object detection.
Using zero-shot, few-shot, and multi-shot prompting strategies, we evaluated
the performance of the model with human annotations serving as the reference
standard. Evaluation metrics included precision, recall, and F1-score. Results
indicate that prompt design considerably affects performance, with recall for
intersection visibility increasing from 21.7% (zero-shot) to 57.0%
(multi-shot). For traffic density, agreement increased from 53.5% to 67.6%. In
stop-sign detection, the model demonstrated high precision (up to 86.3%) but a
lower recall (approximately 76.7%), indicating a conservative response
tendency. Output stability analysis revealed that humans and the model faced
difficulties interpreting structurally ambiguous scenes. However, the model's
explanatory texts corresponded with its predictions, enhancing
interpretability. These findings suggest that, with well-designed prompts, LLMs
hold promise as supportive tools for scene-level driving risk assessments.
Future studies should explore scalability using larger datasets, diverse
annotators, and next-generation model architectures for elderly driver
assessments.
