A Multi-modal Detection System for Infrastructure-based Freight Signal Priority

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.

Dual LiDAR-Based Traffic Movement Count Estimation at a Signalized Intersection: Deployment, Data Collection, and Preliminary Analysis

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.