基于基础设施的雷达系统用于高速公路交通监测的系统性评估
A Systematic Evaluation of Infrastructure-Based Radar System for Highway Traffic Monitoring
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中文总结 AI 辅助
本研究引入DRaT数据集，系统评估基于基础设施的雷达在高速公路交通监测中的检测、跟踪及宏观参数估计性能，发现其精确率78%、召回率57%，并开源数据集以促进可重复研究。
中文摘要 AI 辅助
基于基础设施的雷达系统为交通监测提供了稳健且远距离的解决方案，然而其在真实世界条件下的检测与跟踪性能尚未得到充分评估。本研究引入了DRaT（无人机与雷达轨迹），一个在德克萨斯州沃斯堡的高速公路合流路段收集的自然车辆轨迹双模态数据集，以系统性地评估雷达感知性能与无人机提供的真实数据之间的对比。性能评估在三个层面进行：单个车辆检测、轨迹跟踪以及宏观交通参数估计。在单个车辆检测方面，雷达实现了78%的整体精确率和57%的召回率，在拥堵交通条件下及较长距离时性能有所下降。在轨迹层面，雷达展现出相当强的跟踪性能（IDF1 = 0.699），在成功建立轨迹时能够保持可靠的车辆身份。对于宏观交通流指标，雷达能够准确估计空间平均速度（MAPE < 4%），但由于漏检，对密度和流量的估计约低估了23%。本文还讨论了路边雷达感知系统的实际部署考虑及潜在的下游应用。为支持基于基础设施的感知系统的可重复研究，我们已在Zenodo上开源了DRaT数据集：https://zenodo.org/records/20171110。
英文摘要
Infrastructure-based radar systems offer robust and long-range solutions for traffic monitoring, yet their detection and tracking performance under real-world conditions remains insufficiently evaluated. This study introduces DRaT (Drone and Radar Trajectories), a dual-modality dataset of naturalistic vehicle trajectories collected at a highway merging segment in Fort Worth, Texas, to systematically assess radar sensing performance against drone-derived ground truth. The performance is evaluated at three levels: individual vehicle detection, trajectory tracking, and macroscopic traffic parameter estimation. For individual vehicle detection, the radar achieves an overall precision of 78% and a recall of 57%, with degraded performance under congested traffic conditions and at longer distances. At the trajectory level, the radar demonstrates reasonably strong tracking performance (IDF1 = 0.699), maintaining reliable vehicle identities when tracks are successfully established. For macroscopic traffic flow metrics, the radar accurately estimates space-mean speed (MAPE < 4%) but underestimates density and volume by approximately 23% due to missed detections. The paper also discusses practical deployment considerations and potential downstream applications of roadside radar sensing systems. To support reproducible research on infrastructure-based sensing systems, we have open-sourced the DRaT dataset on Zenodo: https://zenodo.org/records/20171110.
发表机构
- Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。
