使用LiDAR数据进行无实时车辆跟踪
Yibin Zhang1,2, Qiyang Luo3, Shuichao Zhang1,2
1School of Civil and Transportation Engineering, Ningbo University of Technology, Ningbo, Zhejiang, China.
PloS one
|May 9, 2025
概括
这项研究引入了一种使用路边LiDAR数据的新实时车辆跟踪方法. 带有简单在线和实时跟踪 (SORT) 的逐点跟踪方法提高了智能运输系统的准确性和速度.
科学领域:
- 智能运输系统 (ITS) 是一种智能运输系统.
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 准确的车辆轨迹数据对于开发智能交通系统 (ITS) 和支持互联和自动驾驶汽车 (CAV) 至关重要.
- 激光雷达传感器提供实时微观车辆轨迹,但从原始点云精确跟踪是一个重大挑战.
- 现有的跟踪方法通常依赖于边界框,这些框在某些情况下可能是计算密集型和不太准确的.
研究的目的:
- 为路边LiDAR系统提出一种新的实时车辆跟踪机制.
- 为了提高车辆轨迹生成的准确性和计算效率.
- 调整简单的在线和实时跟踪 (SORT) 算法,以使用2D LiDAR数据进行逐点跟踪.
主要方法:
- 开发了一个新的跟踪机制,将2D LiDAR数据结构与SORT算法集成在一起.
- 取代了传统的界限框检测,采用了使用车辆中心点的逐点轨道方法.
- 引入了一种新型索引,该索引包含LiDAR距离测量,用于精确的车辆空间定位.
主要成果:
- 与传统的界限框方法相比,拟议的方法显示出更高的跟踪精度.
- 新的跟踪机制实现了更快的计算速度,提高了实时应用的可行性.
- 在信号交叉点使用32通道便携式LiDAR数据的评估验证了该方法的有效性.
结论:
- 开发的轨道逐点跟踪方法显著改善了从路边LiDAR数据的实时车辆轨迹生成.
- 这种方法为ITS应用程序提供了比传统的界限框方法更准确,更高效的计算替代方案.
- 这些发现表明,互联和自动驾驶汽车的运营能力有前途.
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