评估路边基于LiDAR和基于视觉的多模型全交通轨迹数据
Fei Guan1, Hao Xu1, Yuan Tian2
1Department of Civil & Environmental Engineering, University of Nevada, 1664 N. Virginia St., Reno, NV 89557, USA.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
与计算机视觉系统相比,LiDAR传感器为交通轨迹数据提供了更高的检测范围和夜间精度. 这两种方法都准确地测量了车辆的速度,但LiDAR在行人数据的一致性方面表现出色.
科学领域:
- 运输工程 运输工程
- 传感器技术 传感器技术
- 数据科学数据科学数据科学
背景情况:
- 轨迹数据对于理解时空交通模式至关重要.
- 多模型全流量数据提高了微观分析的准确性和频率.
- 路边传感器如LiDAR和摄像头是数据收集的关键.
研究的目的:
- 为了比较和评估基于LiDAR和基于计算机视觉的轨迹数据.
- 在各种条件下评估传感器性能,包括照明和用户类型.
- 为选择合适的传感器进行交通分析提供指导.
主要方法:
- 从同一十字路口的LiDAR和摄像头传感器收集的轨迹数据.
- 分析了检测范围,不同照明中的准确性和速度测量一致性的数据.
- 应用了光滑技术来评估速度数据.
主要成果:
- 激光雷达显示了更广泛的检测范围和在低光下比计算机视觉更好的性能.
- 这两种传感器在白天对车辆体积计数表现良好.
- 激光雷达在夜间提供了更一致的行人计数准确度,并且两者都准确地测量了车辆光滑后的速度.
结论:
- 激光雷达在探测范围和夜间/行人数据准确性方面具有优势.
- 计算机视觉数据显示,行人速度测量的波动更多.
- 该研究为根据特定交通分析需求选择传感器提供了有价值的见解.
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