Graph Matching-Based Spatiotemporal Calibration of Roadside Sensors in Cooperative Vehicle-Infrastructure Systems

Sensors, such as cameras, millimeter-wave radar, and LiDAR, are widely deployed in cooperative vehicle-infrastructure systems. The demand for calibration of initial installation, damage replacements, and unstable installation has risen dramatically. Traditional methods require on-site operation and road closure; thus, repeated calibration can severely affect traffic conditions and expose operational personnel to potential safety threats. As more and more autonomous vehicles (AVs) flood the roads, this paper proposes an automatic calibration framework of roadside sensors by leveraging the high-precision positioning and perception data of AVs. First, we design a graph-based target-matching algorithm using an AV's surrounding traffic perception data to identify the AV of interest from a dataset of multiple target trajectories recorded by roadside sensors. A line search algorithm is then designed to adjust the clock delay between sensors and establish the temporal correspondence, where a Gaussian process is applied to estimate the vehicle state in continuous time. Finally, we develop a least squares optimization model to complete the final calibration with the AV positioning data. The influence of measurement noise and missed detections on the proposed calibration framework are analyzed in simulated scenarios based on a Next Generation SIMulation (NGSIM) dataset, and the practicability is validated based on real-world data collected at Donghai Bridge, Hangzhou Bay Bridge, and DAIR-V2X dataset. It is shown that the proposed target matching algorithm can identify an AV trajectory from roadside sensor data with 20%-90% higher accuracy than baseline models, and the framework can accurately estimate the spatial and temporal parameters even with poor data quality. The mean least squares error of the trajectory alignment reaches centimeter-level accuracy.