在雾条件下与协作路边单位进行多目标追踪
Tao Shi1,2, Xuan Wang2,3, Wei Jiang2
1State Key Laboratory of Intelligent Vehicle Safety Technology, Chongqing 400023, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了智能路边装置 (RSU) 的新方法,以改善雾条件下的多目标跟踪. 该方法提高了更安全的智能运输系统 (ITS) 的检测准确性和可靠性.
科学领域:
- 运输工程 运输工程
- 计算机视觉 计算机视觉
- 传感器融合式传感器
背景情况:
- 智能路边单位 (RSU) 对智能运输系统 (ITS) 至关重要.
- 路边LiDAR传感器提供高精度,但受到雾的挑战,这降低了性能.
- 雾引起的散射和LiDAR光束的衰减阻碍了多目标跟踪,并危及ITS的安全性.
研究的目的:
- 开发一种增强的协作RSU方法,用于在雾环境中准确的多目标跟踪.
- 通过解决LiDAR在雾中性能恶化的问题,提高ITS的可靠性和安全性.
- 集成拒绝和跟踪功能,以实现基于RSU的强大感知.
主要方法:
- 一个修改后的双边过器可以动态调整内核尺度,以有效地消除点云噪声.
- 一个多RSU合作跟踪框架使用粒子概率假设密度 (PHD) 过器来测量聚变.
- 在智能路边平台上实施多目标追踪系统,用于现实世界的测试.
主要成果:
- 拟议的方法显著提高了目标检测准确度8% (薄雾) 和29% (厚雾) 与雾噪声消除后的统计过相比.
- 该系统在跟踪多类目标方面表现出强的性能.
- 比最先进的方法显示出优越性,特别是在HOTA,MOTA和IDs等高阶指标中.
结论:
- 开发的协作RSU方法有效地提高了多目标跟踪在雾条件下的准确性和可靠性.
- 这种方法有助于提高智能运输系统的安全性和性能.
- 该算法显示出优异的实时性能和实用ITS应用的稳定性.
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