Automating Crash Diagram Generation Using Vision-Language Models: A Case Study on Multi-Lane Roundabouts
Crash diagrams are essential tools in transportation safety analysis, yet their manual preparation remains time-consuming and prone to human variability. This study investigates the use of Vision-Language Models (VLMs) to automate crash diagram generation from police crash reports, focusing on multilane roundabouts as a challenging test case. A three-part structured prompt framework was developed to guide model reasoning through interpretation, extraction, and visual synthesis, while a 10-metric evaluation system was designed to assess diagram quality in terms of semantic accuracy, spatial fidelity, and visual clarity. Three popular models, including GPT-4o, Gemini-1.5-Flash, and Janus-4o...
| Authors | Xiao Lu, Hao Zhen, Jidong J. Yang |
|---|---|
| Primary category | cs.HC |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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