Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also manage these situations by behaving cooperatively and naturally. In this paper, we present a novel learning-based method to efficiently estimate other vehicles' intentions and interact with them in ramp merging scenarios, without over-the-air communication between vehicles. The intention estimate is generated from a Probabilistic Graphical Model (PGM) which organizes historical data and latent intentions and determines predictions. Real driving trajectories are used to learn transition models in the PGM. Thus, besides the structure of the PGM, our method does not require human-designed reward or cost functions. The PGM-based intention estimation is followed by an off-the-shelf ACC distance keeping model to generate proper acceleration/deceleration commands. The PGM plays a plug-in role in our self-driving framework [1]. We validate the performance of our method both on real merging data and using a designed merging strategy in simulation, and show significant improvements compared with previous methods. Parameter design is also discussed by experiments. The new method is computationally efficient, and does not require acceleration information about other vehicles, which is hard to read directly from sensors mounted on the autonomous vehicle.