基于ETC大数据的超视界潜在安全威胁车辆识别方法
Guanghao Luo1,2, Fumin Zou1,2, Feng Guo1,2
1Fujian Key Laboratory of Automotive Electronics and Electric Drive, Fujian University of Technology, FuZhou, 350108, China.
Heliyon
|October 9, 2023
概括
本研究引入了一种使用电子收费大数据的新方法,用于识别高速公路上低速车辆的潜在安全威胁. 该系统准确预测车辆的速度和位置,提高交通安全和避免风险.
科学领域:
- 智能运输系统 智能运输系统
- 道路安全工程 道路安全工程
- 数据科学在运输中的应用
背景情况:
- 目前的智能汽车传感器,如LIDAR,难以在视觉范围之外检测低速车辆,这在高速公路上构成安全风险.
- 低速驾驶,有限的可见度和道路设计导致事故,突出显示了先进威胁检测的必要性.
研究的目的:
- 建议使用电子收费 (ETC) 大数据进行超视界潜在安全威胁车辆识别方法.
- 通过解决当前车辆传感技术的局限性,提高驾驶的安全性和舒适性.
主要方法:
- 开发了一个三层系统:车辆速度传感 (wlp-XGBoost),中途车辆位置估计 (DR-HMM) 和多信息融合用于威胁识别.
- 该方法使用ETC平台上的Quanxia部分的实时ETC数据进行了验证,模拟了动态交通条件.
主要成果:
- 该系统在识别潜在的安全威胁车辆方面实现了超过95%的准确性和回忆.
- 在各种路段和交通情况中,可以准确预测车辆的速度和位置.
- 实现了基于威胁车辆趋势的交通状况变化的实时感知.
结论:
- 拟议的ETC大数据驱动方法有效地实时识别低速车辆对安全的潜在威胁.
- 该系统为高速公路的速度规划和风险避免提供了关键的参考.
- 这些发现有助于改善整体道路安全和驾驶体验.
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