<ul><li>The accuracy and reliability of vehicle localization on roads are crucial for applications such as self-driving cars, toll systems, and digital tachographs.</li><li>Recent approaches based on machine learning (ML) have shown superior performance in monitoring interference, but their feasibility in real-world applications and environments has yet to be assessed.</li><li>This study describes large-scale measurement campaigns conducted in real-world settings to evaluate supervised ML-based methods and their performance in real-world settings.</li><li>The study also explores the challenges of combining datasets, outlier detection, and data augmentation techniques in adapting ML models to changes in the datasets.</li></ul>

Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies

Discover more