Simulative Validation of Snapshot Likelihood based GNSS Probability Grid Positioning for Precise Vehicle Localization

High-precision, lane-level absolute positioning of traffic participants in Intelligent Traffic Systems (ITS) and in the context of highly automated driving (HAD) is necessary for various applications. Determining global user position is usually based on Global Navigation Satellite Systems (GNSS) which are prone to multiple error influences especially in urban scenarios. Conventional position estimation methods assume normally distributed observations and do not include a-priori state space information in form of a given map in a tightly coupled manner but rather perform a geometrical map matching after GNSS position was estimated independently. The work presented discusses the applicability of a likelihood based GNSS positioning approach utilizing pseudorange measurements and an ordered grating of the state space which results in a novel snapshot Probability Grid Positioning (PGP) method. For ITS applications, the state space is given by three-dimensional coordinates of the road network. The proposed positioning estimation is validated using a GNSS simulation which also enables the investigation of different Probability Grid (PG) parameterizations and the influence of different error sources according to the GNSS error budget. The accuracy performance of proposed method is validated quantitatively and compared to a conventional positioning method.