The number of research papers on decision-making systems in automated driving has increased significantly over the last few years. Decision-making for automated driving can be performed at different levels: (i) strategic level: generating the optimal route up to the destination; (ii) tactical level: identifying and ranking feasible high-level maneuvers that the vehicle can perform, considering the dynamic objects that are in the surroundings; (iii) operational level: generating a collision-free trajectory (path and speed profile) up to the planning horizon; (iv) stability level: computing the motion control commands for tracking the trajectory. Additionally, supervision can be understood as a combination of one or more decision-making levels. Previous reviews have focused either on one of the levels of decision-making or on a specific environment where the approaches are applied, without any distinction between the contexts in which they are applied (robotics, unmanned vehicles or automated driving). This review studies the state-of-the-art on the decision-making approaches applied specifically to automated driving, during the last lustrum.