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基于连接的车载数据的动态路段风险识别模型

Yongjian Zhang^1,2, Ge Yang³, Tian Xie⁴

¹School of Civil Engineering, Central South University of Forestry & Technology, Changsha, China.

|October 6, 2025

概括

本研究介绍了车载单元和贝叶斯网络模型,使用高级驾驶辅助系统 (ADAS) 数据识别道路风险. 这些模型通过分析驾驶行为和接近撞车事件,有效地评估路段风险.

科学领域:

道路安全工程道路安全工程
交通运输数据科学数据科学
交通运输中的人工智能

背景情况:

先进的驾驶辅助系统 (ADAS) 为了解驾驶行为产生了有价值的数据.
识别动态路段风险对于提高交通安全至关重要.
现有的方法可能无法完全捕捉驾驶行为和道路风险之间的复杂相互作用.

研究的目的:

开发一个动态系统来识别道路路段的风险.
分析不同类型的道路 (城市,高速公路,高速公路) 驾驶行为和道路风险之间的关系.
使用现实世界驾驶数据创建道路安全的预测模型.

主要方法:

设计了一个车载单元,从配备ADAS的车辆收集动态驾驶行为数据.
用定义的标准将道路划分为城市,高速公路和高速公路类别.
利用贝叶斯网络 (BN) 模型,DBSCAN集群,以及近碰撞事件分析 (制动减速,碰撞时间).

主要成果:

为城市道路,高速公路和高速公路开发了单独的BN模型.
成功地根据严重程度识别和分类了接近撞车事件.
匹配的驾驶行为数据和加权的接近碰撞事件来评估道路段的风险水平.
模型对观察到的数据节点表现出高灵敏度.

结论:

拟议的系统有效地使用动态驾驶数据识别路段风险.
贝叶斯网络模型为分析驾驶行为和道路安全提供了强大的框架.
这种方法为实时道路风险评估和主动安全措施提供了一个有希望的方法.

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