{"ID":2843852,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06892","arxiv_id":"2511.06892","title":"Multi-Agent AI Framework for Road Situation Detection and C-ITS Message Generation","abstract":"Conventional road-situation detection methods achieve strong performance in predefined scenarios but fail in unseen cases and lack semantic interpretation, which is crucial for reliable traffic recommendations. This work introduces a multi-agent AI framework that combines multimodal large language models (MLLMs) with vision-based perception for road-situation monitoring. The framework processes camera feeds and coordinates dedicated agents for situation detection, distance estimation, decision-making, and Cooperative Intelligent Transport System (C-ITS) message generation. Evaluation is conducted on a custom dataset of 103 images extracted from 20 videos of the TAD dataset. Both Gemini-2.0-Flash and Gemini-2.5-Flash were evaluated. The results show 100\\% recall in situation detection and perfect message schema correctness; however, both models suffer from false-positive detections and have reduced performance in terms of number of lanes, driving lane status and cause code. Surprisingly, Gemini-2.5-Flash, though more capable in general tasks, underperforms Gemini-2.0-Flash in detection accuracy and semantic understanding and incurs higher latency (Table II). These findings motivate further work on fine-tuning specialized LLMs or MLLMs tailored for intelligent transportation applications.","short_abstract":"Conventional road-situation detection methods achieve strong performance in predefined scenarios but fail in unseen cases and lack semantic interpretation, which is crucial for reliable traffic recommendations. This work introduces a multi-agent AI framework that combines multimodal large language models (MLLMs) with v...","url_abs":"https://arxiv.org/abs/2511.06892","url_pdf":"https://arxiv.org/pdf/2511.06892v1","authors":"[\"Kailin Tong\",\"Selim Solmaz\",\"Kenan Mujkic\",\"Gottfried Allmer\",\"Bo Leng\"]","published":"2025-11-10T09:43:06Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}