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Spatial synchronization in roadside scenarios is essential for integratingdata from multiple sensors at different locations. Current methods usingcascading spatial transformation (CST) often lead to cumulative errors inlarge-scale deployments. Manual camera calibration is insufficient and requiresextensive manual work, and existing methods are limited to controlled orsingle-view scenarios. To address these challenges, our research introduces aparallel spatial transformation (PST)-based framework for large-scale,multi-view, multi-sensor scenarios. PST parallelizes sensor coordinate systemtransformation, reducing cumulative errors. We incorporate deep learning forprecise roadside monocular global localization, reducing manual work.Additionally, we use geolocation cues and an optimization algorithm forimproved synchronization accuracy. Our framework has been tested in real-worldscenarios, outperforming CST-based methods. It significantly enhanceslarge-scale roadside multi-perspective, multi-sensor spatial synchronization,reducing deployment costs.

A Practical Large-Scale Roadside Multi-View Multi-Sensor Spatial Synchronization Framework for Intelligent Transportation Systems