Automated driving technology has attracted extensive interests in both academia and industry. High-definition (HD) map plays the critical basis in ensuring the accuracy perception and localization for automated driving. Considering the time-sensitivity, location and direction-dependency, the dissemination of HD map is a challenging task. In this paper, we address the efficient transmission of HD map data from road side units (RSUs) to vehicles passing through their communication coverage. By jointly considering the age of information (AoI) and the direction of information (DoI) for all vehicles, we propose a deep-reinforcement learning based method to achieve efficient dissemination of HD map data, where we use the deep reinforcement learning to optimize the selection of target vehicles, then optimize the power allocation among all selected vehicles. Simulation results show the performance improvement of our proposed method compared with several existing counterparts. In specific, our proposed method can improve the ratio of vehicles that obtains the map data of the next area by 229% compared with random scheme with the number of vehicles n=4.