Cooperative Calibration for Enhanced Localization Services in Transport

Forskningsprojekt, 2025

This 12-month project aims to revolutionize localization services and transportation safety in 5G/6G networks by addressing the inadequate calibration of network infrastructure originally designed for communication. Using vehicle sensors (e.g., GPS, Lidar) and inter-vehicle wireless measurements, the project will develop efficient online calibration methods for the geometric and hardware parameters of the infrastructure. It employs a mix of model-based and machine learning approaches across three tasks: representing calibration parameters, performing non-cooperative calibration with single devices, and enhancing calibration efficiency through inter-vehicle cooperation. The project unites experts in radio localization, machine learning, distributed systems, and hardware modeling to deliver an efficiently calibrated infrastructure, benefiting the transportation ecosystem.