Lessons Learned from the Real-World Deployment of Multisensor Fusion for Proactive Work Zone Safety Application

Abstract

Proactive safety systems that anticipate and mitigate traffic risks before incidents occur are increasingly recognized as essential for improving work zone safety. Unlike traditional reactive safety approaches, proactive systems rely on real-time sensing, trajectory prediction, and intelligent infrastructure to detect potential safety hazards. Existing simulation-based and real-world deployment studies often overlook and rarely discuss the practical challenges associated with deploying such proactive systems in operational settings, particularly those involving roadside infrastructure enabled multisensor integration and fusion. This study addresses that gap by presenting deployment insights and technical lessons learned from a real-world implementation of a proactive safety system utilizing multiple sensors mounted on a roadside infrastructure at an active bridge repair work zone along the N-2/US-75 corridor in Nebraska, USA. The deployed system combines lidar, radar, and camera sensors with an edge computing platform to support multimodal object tracking, trajectory fusion, and real-time predictive analytics. Specifically, this study presents key lessons learned across three critical stages of deployment: 1) sensor selection and placement; 2) sensor calibration, system integration, and validation; and 3) sensor fusion. Additionally, we propose a predictive digital twin framework that uses fused trajectory data to predict vehicle path for enabling early conflict detection and real-time warning generation, thereby enabling proactive safety interventions.