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.

翻译：货运车辆接近信号化交叉口时，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。准确及时地感知车辆类型、位置与速度对于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摄像头的基于基础设施多模态货运车辆检测系统的设计、部署与评估方案。系统采用混合传感架构，由交叉口安装子系统与路段中间子系统组成，通过无线通信实现同步数据传输。感知流程融合了基于聚类与基于深度学习的检测方法，并采用卡尔曼滤波跟踪以实现稳定的实时性能。激光雷达测量数据被配准至大地参考坐标系，以支持车道级定位与连续的车辆跟踪。现场评估表明，该系统能够以高时空分辨率可靠监测货运车辆运行状态。该设计与部署为开发支持货运信号优先应用的基于基础设施传感系统提供了实践参考。