Infrastructure Sensor-enabled Vehicle Data Generation using Multi-Sensor Fusion for Proactive Safety Applications at Work Zone

Abstract

Infrastructure-based sensing and real-time trajectory generation show promise for improving safety in high-risk roadway segments such as work zones, yet practical deployments are hindered by perspective distortion, complex geometry, occlusions, and costs. This study tackles these barriers by integrating roadside camera and LiDAR sensors into a cosimulation environment to develop a scalable, cost-effective vehicle detection and localization framework, and employing a Kalman Filter-based late fusion strategy to enhance trajectory consistency and accuracy. In simulation, the fusion algorithm reduced longitudinal error by up to 70 percent compared to individual sensors while preserving lateral accuracy within 1 to 3 meters. Field validation in an active work zone, using LiDAR, a radar-camera rig, and RTK-GPS as ground truth, demonstrated that the fused trajectories closely match real vehicle paths, even when single-sensor data are intermittent or degraded. These results confirm that KF based sensor fusion can reliably compensate for individual sensor limitations, providing precise and robust vehicle tracking capabilities. Our approach thus offers a practical pathway to deploy infrastructure-enabled multi-sensor systems for proactive safety measures in complex traffic environments.