用双激光雷达系统在路口精准统计车流，提升信号灯优化数据质量。
Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis
- 部署双激光雷达获取3D车辆位置，按方向/类型分类计数。
- 实测数据揭示车流趋势与异常，支持信号优化决策。
- 适合交通工程、智能路口研发人员参考应用。
交叉口的交通流计数（TMC）对优化信号配时、评估交通控制效果及提出高效车道配置至关重要。传统方法如人工计数、地感线圈、气动路钉和基于摄像头的识别存在局限，尤其在恶劣天气或夜间光照不足时精度下降。相比之下，激光雷达（LiDAR）因成本降低和在三维目标检测、跟踪等领域的广泛应用而日益普及。本文报告了在加州里阿尔托市一个路口部署并评估双LiDAR系统的成果，利用两台LiDAR的3D边界框检测结果，实现按交通方向、车辆行驶轨迹和车辆类别进行车辆计数。研究讨论了估算的交通流数据及其呈现的趋势与异常，并提出了可能改进方向，以进一步提升交通流计数、轨迹预测与驾驶意图识别能力。
原文摘要 · Abstract (English)
Traffic Movement Count (TMC) at intersections is crucial for optimizing signal timings, assessing the performance of existing traffic control measures, and proposing efficient lane configurations to minimize delays, reduce congestion, and promote safety. Traditionally, methods such as manual counting, loop detectors, pneumatic road tubes, and camera-based recognition have been used for TMC estimation. Although generally reliable, camera-based TMC estimation is prone to inaccuracies under poor lighting conditions during harsh weather and nighttime. In contrast, Light Detection and Ranging (LiDAR) technology is gaining popularity in recent times due to reduced costs and its expanding use in 3D object detection, tracking, and related applications. This paper presents the authors' endeavor to develop, deploy and evaluate a dual-LiDAR system at an intersection in the city of Rialto, California, for TMC estimation. The 3D bounding box detections from the two LiDARs are used to classify vehicle counts based on traffic directions, vehicle movements, and vehicle classes. This work discusses the estimated TMC results and provides insights into the observed trends and irregularities. Potential improvements are also discussed that could enhance not only TMC estimation, but also trajectory forecasting and intent prediction at intersections.
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