使用代用安全措施和不合规行为进行交叉路口的积极安全评估的框架
Deep Patel1, Parisa Hosseini1, Mohammad Jalayer2
1Department of Civil and Environmental Engineering, Rowan University, United States.
Accident; analysis and prevention
|September 6, 2023
概括
这项研究引入了一个AI视频分析工具,通过跟踪道路用户行为和冲突来评估十字路口的安全性. 它识别了风险行为,估计了碰撞的可能性,有助于改善交通安全.
科学领域:
- 交通工程是交通工程.
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 十字路口是关键安全区,超过50%的致命事故发生在那里.
- 了解道路使用者行为和冲突对于提高交通安全至关重要.
- 现有的交叉路口安全分析方法需要先进的工具来进行全面的数据收集.
研究的目的:
- 开发一个人工智能驱动的视频分析工具来评估交叉路口的安全性.
- 利用代用安全措施,并识别不合规行为.
- 根据安全性估计潜在的碰撞频率和排名交叉点.
主要方法:
- 部署了YOLO-v5和DeepSORT用于实时轨迹检测和跟踪.
- 从新泽西州格拉斯伯勒的六个信号交叉点收集了54小时的视频数据.
- 应用极端值理论 (EVT) 用PET和TTC等替代安全措施来估计机数量.
主要成果:
- 在检测和跟踪道路使用者轨迹方面实现了92%-98%的准确性.
- 量化交通和行人数量,红灯行驶以及行人不遵守规定.
- 提供了关键的安全措施 (PET,TTC) 并根据事故严重程度的分数排列交叉点.
结论:
- 人工智能工具有效评估交叉路口的安全性,并识别风险行为.
- 使用EVT和替代安全措施进行撞车估计为交通工程师提供了宝贵的见解.
- 开发的排名系统有助于优先考虑高风险十字路口的安全干预措施.
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