Ego-lane estimation for lane-level navigation in urban scenarios

Future lane-precise navigation systems will recommend lane changes to drivers if needed. To achieve this, robust lane-level localization on a navigable map is essential. We propose an ego-lane estimation algorithm to robustly determine the ego-lane in urban scenarios based on a particle filter approach. The method only requires sensors available in a current production car, i.e. visual lane-marking detection, radar, and GPS, and a digital map describing road geometry and topology. Extensive experimental validation has shown an error rate of less than 0.75% with an availability of 95% of the total time and below 0.4% at 96% availability in situations most relevant for navigation. The influence of the used sensors has been evaluated.