将LiDAR传感器数据集成到微仿真模型校准中,用于主动安全分析
Morris Igene1, Qiyang Luo1, Keshav Jimee1
1Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.
Sensors (Basel, Switzerland)
|July 13, 2024
概括
这项研究将光检测和测距 (LiDAR) 数据与用于交通安全分析的微模拟模型集成在一起. 校准的模型准确地预测了后端碰撞,改善了运输安全策略.
科学领域:
- 运输工程 运输工程
- 交通安全分析 交通安全分析
- 数据科学数据科学数据科学
背景情况:
- 车辆轨迹数据对于校准交通微型模拟模型至关重要.
- 光探测和测距 (LiDAR) 提供高分辨率的3D环境数据,适用于自动驾驶汽车等实时应用.
- 由于其速度,精度和在各种条件下的性能,LiDAR比传统方法具有优势.
研究的目的:
- 开发一个框架,将LiDAR传感器数据集成到用于主动安全分析的模拟模型中.
- 使用LiDAR提取的车辆轨迹数据校准微模模型.
- 评估校准模型在预测交通安全事件方面的有效性.
主要方法:
- 车辆轨迹数据从六个城市十字路口的LiDAR点云中提取.
- 使用了PTV VISSIM软件和Wiedemann 1999汽车跟踪和变换车道模型.
- 定向粗力法校准了模型参数,达到92.7%的平均精度.
- 来自校准模型的后端冲突与历史事故数据相结合,并使用负二项模型进行分析.
主要成果:
- 校准的微型模拟模型准确地复制了观察到的交通场景.
- 后端冲突计数是所有十字路口观察到的后端碰撞频率的统计学上显著的预测指标.
- 该研究证明了使用LiDAR数据进行详细的交通分析的可行性.
结论:
- 综合框架为使用LiDAR数据和微显模拟进行主动安全评估提供了强有力的方法.
- 这种方法提高了更安全的运输系统和交通控制策略的设计.
- 这些发现对寻求预测和减轻交通拥堵和安全问题的运输专业人士来说非常有价值.
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