融合激光雷达与摄像头，实现货运车辆高精度实时检测
A Multi-modal Detection System for Infrastructure-based Freight Signal Priority
- 采用激光雷达与摄像头的混合传感架构，分段部署并无线同步
- 实现车道级定位与稳定实时追踪，支持货运信号优先控制
- 为智能交通中的货运优先系统提供可落地的感知方案
接近信号交叉口的货运车辆需要可靠的检测与运动估计，以支持基于基础设施的货运信号优先（FSP）。准确及时地感知车辆类型、位置和速度，对实现有效的优先控制策略至关重要。本文提出了一种集成激光雷达与相机传感器的基于基础设施的多模态货运车辆检测系统，包含路口安装的子系统与道路中段的子系统，通过无线通信实现数据同步。感知流程结合基于聚类与深度学习的检测方法，并使用卡尔曼滤波进行跟踪，实现稳定实时性能。激光雷达数据被注册到大地坐标系中，支持车道级定位与一致的车辆追踪。实地评估表明，该系统能以高时空分辨率可靠监测货运车辆运行。设计与部署过程为开发支持FSP应用的基础设施感知系统提供了实用经验。
原文摘要 · Abstract (English)
Freight vehicles approaching signalized intersections require reliable detection and motion estimation to support infrastructure-based Freight Signal Priority (FSP). Accurate and timely perception of vehicle type, position, and speed is essential for enabling effective priority control strategies. This paper presents the design, deployment, and evaluation of an infrastructure-based multi-modal freight vehicle detection system integrating LiDAR and camera sensors. A hybrid sensing architecture is adopted, consisting of an intersection-mounted subsystem and a midblock subsystem, connected via wireless communication for synchronized data transmission. The perception pipeline incorporates both clustering-based and deep learning-based detection methods with Kalman filter tracking to achieve stable real-time performance. LiDAR measurements are registered into geodetic reference frames to support lane-level localization and consistent vehicle tracking. Field evaluations demonstrate that the system can reliably monitor freight vehicle movements at high spatio-temporal resolution. The design and deployment provide practical insights for developing infrastructure-based sensing systems to support FSP applications.
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