联网车辆信息能否减少在雾高速公路条件下超视距离撞车风险? 一项基于极端价值理论的研究
Wenhao Ren1, Xiaohua Zhao1, Ying Yao1
1Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China.
Accident; analysis and prevention
|April 26, 2025
概括
联网车辆信息 (CVI) 有效地降低了雾高速公路上的撞车风险,特别是在视觉范围之外的条件下. 极端价值理论 (EVT) 的分析证实了CVI的说法.
科学领域:
- 交通安全工程 交通安全工程
- 智能运输系统 智能运输系统
- 极端价值理论应用 极端价值理论应用
背景情况:
- 联网车辆信息 (CVI) 旨在提高交通安全.
- 在雾,超视距高速公路条件下,CVI的安全影响仍然不清楚.
研究的目的:
- 评估和量化CVI对雾,超视频距离高速公路场景中的交通安全的影响.
- 应用极端价值理论 (EVT) 进行风险评估和影响分析.
主要方法:
- 利用驾驶模拟技术创建一个连接的环境实验平台.
- 在雾条件下的传统和联网环境中收集驾驶行为数据.
- 应用了EVT的峰值超过值方法来建模时间到碰撞 (TTC) 和减速率以避免碰撞 (DRAC) 等指标.
主要成果:
- 基于DRAC的EVT模型显示出卓越的数据拟合性能和可靠性.
- 在雾的高速公路上,在视觉范围之外的事件中,CVI被发现有效地减轻了碰撞风险.
- 在不同驾驶群体中观察到碰撞风险和CVI有效性的变化.
结论:
- 在具有挑战性的条件下,EVT提供了一个强大的框架来分析CVI的安全影响.
- 在雾的高速公路上,CVI显著提高了超视距离事件的安全性.
- 了解CVI的机制对于优化其应用和提高驾驶员安全至关重要.
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