Probabilistic error model for a lane marking based vehicle localization coupled to open source maps

Abstract

Recent works have focused on lane marking feature based vehicle localization using enriched maps. The localization precision of existing methods depends strongly on the accuracy of the maps which are specially customized. In this article, the authors propose a marking feature based vehicle localization using open source map. This method makes use of multi-criterion confidences to infer potential errors, and in advance, to enhance the vehicle localization. At first, the vision-based lane marking models are obtained. Meanwhile, the map-based lane markings of current state are derived from map databases. Both lane marking sources are fused together to implement vehicle localization, using a multi-kernel based algorithm. In order to further improve the localization performance, a probabilistic error model is employed to identify the possible errors. The experiments have been carried out on public database. The results show that error modeling leads to a lower average lateral error in localization result.