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

TL;DR

This paper introduces a multi-modal detection system combining LiDAR and camera sensors for accurate, real-time freight vehicle detection at intersections to enable effective signal priority control.

Contribution

It presents a novel hybrid sensing architecture with synchronized data transmission and a perception pipeline integrating clustering, deep learning, and Kalman filtering for infrastructure-based freight detection.

Findings

Reliable freight vehicle monitoring at intersections

High spatio-temporal resolution detection

Effective support for Freight Signal Priority

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.