FCO based localization using vehicle environment data

To facilitate advanced driver assistance systems and autonomous driving, it is important to know the current position of a vehicle relative to the street network the vehicle is driving on. The default method for determining the current position of a vehicle is through the use of Global Navigation Satellite Systems. Especially in dense urban settings, satellite signals can be damped or reflected by buildings, leading to degradation in the quality of the position estimation. In this paper, we present a method to increase the accuracy of satellite positioning by utilizing vehicle sensor data. This can be accomplished by matching the position and orientation of known vehicles relative to the observer onto a digital map of the street lanes. By determining the most likely position of the constellation of all known vehicles on the map, the current position of the observer can be estimated. To test our method, we simulated floating car observers (FCO) in traffic with different traffic volumes and lane geometries. The results of our method were compared to a simple satellite positioning map matching algorithm.