In this work, we present a decision-making system for automated vehicles driving in highway environments. The task is modeled as a Partially Observable Markov Decision Process, in which the physical states and intentions of surrounding traffic are uncertain. The problem is solved in an online fashion using Monte Carlo tree search. At each decision step, a search tree of beliefs is incrementally built and explored in order to find the current best action for the ego-vehicle. The beliefs represent the predicted state of the world as a response to the actions of the ego-vehicle and are updated using an interaction-and intention-aware probabilistic model. To estimate the long-term consequences of any action, we rely on a lightweight model-based prediction of the scene that assumes risk-averse behavior for all agents. We refer to the proposed decision-making approach as human-like, since it mimics the human abilities of anticipating the intentions of surrounding drivers and of considering the long-term consequences of their actions based on an approximate, common-sense, prediction of the scene. We evaluate the proposed approach in two different navigational tasks: lane change planning and longitudinal control. The results obtained demonstrate the ability of the proposed approach to make foresighted decisions and to leverage the uncertain intention estimations of surrounding drivers.