Prediction of Real-Time Kinematic Positioning Availability on Road Using 3D Map and Machine Learning

Real-Time Kinematic (RTK) positioning is a precise positioning method, which is expected to support self-driving. However, it is known that the availability of RTK highly depends on the Global Navigation Satellite System (GNSS) signal environment, which is influenced by buildings and viaduct of tunnel. Before driving, it is convenience if we can simulate the GNSS signal environment using a three-dimensional (3D) map and predict the availability of RTK. It is also important to know the limitation of RTK for other sensors. Therefore, we predicted it using machine learning based on the past test-driving and simulated signal environment datasets. The prediction accuracy was almost 65-80% from two evaluation tests in Tokyo and we found several new issues to consider for RTK availability prediction.