GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input

Sammanfattning: In Intelligent Transport Systems (ITS), specifically in autonomous driving operations, accurate vehicle localization is essential for safe operations. The localization accuracy depends on both position and positioning error estimates. Technologies aiming to improve positioning error estimation are required and are currently being researched. This project has investigated machine learning algorithms applied to positioning error estimation by assessing relevant information obtained from a GNSS receiver and adding environmental information  coming from a camera mounted on a radio controlled vehicle testing platform. The research was done in two stages. The first stage consists of the machine learning algorithms training and testing on existing GNSS data coming from Waysure´s data base from tests ran in 2016, which did not consider the environment surrounding the GNSS receiver used during the tests. The second stage consists of the machine learning algorithms training and testing on GNSS data coming from new test runs carried on May 2019, which include the environment surrounding the GNSS receiver used. The results of both stages are compared. The relevant features are obtained as a result of the machine learning decision trees algorithm and are presented. This report concludes that there is no statistical evidence indicating that the tested environmental input from the camera could improve positioning error estimation accuracy with the built machine learning models.