用多智能体框架结合视觉与大模型，实现交通场景的自动识别与消息生成。
Multi-Agent AI Framework for Road Situation Detection and C-ITS Message Generation
- 设计多智能体系统，分工完成感知、判断、决策和消息生成。
- 检测召回率100%，消息格式正确率满分，但存在误检和细节识别不足。
- 发现小模型更适配交通任务，适合关注智能交通落地的研究者。
传统道路状况检测方法在预设场景中表现良好，但在未见场景下失效且缺乏语义解释，难以支撑可靠交通建议。本文提出一种多智能体AI框架，融合多模态大语言模型（MLLM）与视觉感知技术，用于道路状况监控。该框架处理摄像头输入，协调专用智能体完成场景检测、距离估计、决策制定及合作式智能交通系统（C-ITS）消息生成。在从TAD数据集20段视频中提取的103张图像上进行评估，分别测试了Gemini-2.0-Flash与Gemini-2.5-Flash模型。结果表明，场景检测召回率达100%，消息结构正确性为100%；但两类模型均存在误检问题，且在车道数量、行驶车道状态及原因代码等细节上表现下降。意外的是，尽管Gemini-2.5-Flash通用能力更强，其检测准确率与语义理解反而低于Gemini-2.0-Flash，且延迟更高（表二）。这些发现提示需针对智能交通场景优化专用大模型或多模态模型。
原文摘要 · Abstract (English)
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.
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