The current state of the art in automotive high definition digital (HD) map generation based on dedicated mapping vehicles cannot reliably keep these maps up to date because of the low traversal frequencies. Anonymized data collected from the fleet of vehicles that is already on the road provides a huge potential to outperform such state of the art solutions in robustness, safety and up-to-dateness of the map while achieving comparable quality. We thus present a solution based on crowdsourced data to (i) detect changes in the map independent of the type of change, (ii) automatically trigger map update jobs for parts of the map, and (iii) create and integrate map patches to keep the map always up to date. The developed solution provides a crowdsourced up to date HD map to make reliable prior information on lane markings and road edges available to automated driving functions.