A Data-Driven Generative Model for GPS Sensors for Autonomous Driving

Autonomous driving (AD) is envisioned to have a significant impact on people's life regarding safety and comfort. Positioning is one of the key challenges in realizing AD, where global navigation systems (GNSS) is traditionally used as an important source of information. The area of GNSS are well explored and the different sources of error are deeply investigated. However the existing modeling methods often have very comprehensive requirements for the training data where all affecting conditions such as ephemeris data should be well known. The main goal of this paper is to develop a solution to model GPS error that only requires information which is available in the vehicle without having access to detailed information about the conditions. We propose a statistical generative model using autoregression and Gaussian mixture models and develop a learning algorithm to estimate the parameters using the data collected in real traffic. The proposed model is evaluated by comparing the produced artificial data with the validation data collected at different traffic conditions and the results indicate that the model is successfully mimicking the sensor behavior.