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

2026-02-19

AI Summary

This study addresses the lack of high-precision real-time perception of freight vehicles at signalized intersections, which hinders infrastructure-based signal priority control. To overcome this limitation, the authors propose a hybrid roadside sensing architecture that fuses LiDAR and camera data. The system employs two coordinated subsystems—one at the intersection and another at the mid-segment of the approach—to enable lane-level, real-time monitoring of freight vehicle type, position, and speed. Key components include clustering and deep learning–based detection, point cloud georeferencing, Kalman filter–based tracking, and wireless synchronization for temporal alignment. Field tests demonstrate that the proposed system achieves high stability and spatiotemporal resolution, offering a practical and effective technical solution for freight signal priority control.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Application Domains: Transportation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Security and Privacy: Large-scale security measurements
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• Freight Signal Priority
• vehicle detection
• infrastructure-based sensing
• multi-modal perception
• signalized intersections

Innovation

• multi-modal sensing
• Freight Signal Priority (FSP)
• LiDAR-camera fusion
• hybrid sensing architecture
• geodetic localization