从事故报告到更安全的道路:一个多式联运框架,整合视觉语言模型和街景分析
Guanhe Wu1, Xintong Yan1, Yajie Zou2
1Department of Civil and Environmental Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.
Accident; analysis and prevention
|January 24, 2026
概括
这项研究引入了一个新的多式联运框架,使用视觉语言模型来分析事故报告,识别根本原因,以改善道路安全诊断,超出简单的事故计数.
科学领域:
- 道路安全分析的分析
- 交通运输中的人工智能
- 多式联运数据融合技术
背景情况:
- 传统的道路安全分析依赖于汇总的数据,限制了对个别碰撞机制的理解.
- 崩叙述和图表包含丰富的因果信息,通常未得到充分利用.
- 现有的方法很难解释特定事故的根本原因.
研究的目的:
- 为可扩展和可解释的道路风险诊断提出一个以机制为导向的多式联运框架.
- 开发一种方法来区分即时的崩触发器和上下文约束.
- 为了实现实际的,机制驱动的道路安全诊断.
主要方法:
- 利用视觉语言模型共同分析碰撞叙述和图表.
- 建立了明确的推理链,以确定根本原因.
- 分解的碰撞是人类,车辆,道路,环境和交通信号因素的贡献.
- 空间聚合属性以确定高风险位置,并分析街景图像以查找设计缺陷.
主要成果:
- 视觉语言模型显著超过了基线回归模型.
- 格洛克2多式联运车型显示了与专家注释的最高一致性.
- 推断的根本原因关键词有效地解释了因素贡献.
- 街景分析成功地确定了现实世界的工程问题.
结论:
- 将因果推理与多式交通事故数据相结合,提供了实用的,机制驱动的道路安全诊断.
- 拟议的框架使风险诊断能够超越传统的基于频率的查.
- 这种方法提高了对道路交通事故的理解和预防.
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