  Spatial synchronization in roadside scenarios is essential for integrating
data from multiple sensors at different locations. Current methods using
cascading spatial transformation (CST) often lead to cumulative errors in
large-scale deployments. Manual camera calibration is insufficient and requires
extensive manual work, and existing methods are limited to controlled or
single-view scenarios. To address these challenges, our research introduces a
parallel spatial transformation (PST)-based framework for large-scale,
multi-view, multi-sensor scenarios. PST parallelizes sensor coordinate system
transformation, reducing cumulative errors. We incorporate deep learning for
precise roadside monocular global localization, reducing manual work.
Additionally, we use geolocation cues and an optimization algorithm for
improved synchronization accuracy. Our framework has been tested in real-world
scenarios, outperforming CST-based methods. It significantly enhances
large-scale roadside multi-perspective, multi-sensor spatial synchronization,
reducing deployment costs.
