For automated drivingGovernorAutomated driving, decision-making determines the next maneuverManeuver that the vehicle should execute, for which the motionMotion planner will generate a trajectory. The feasibility of the maneuverManeuver depends on the current conditions of the vehicle, the route, and the traffic. Thus, decision-making must determine which maneuvers are feasibleFeasible with relatively simple calculations, so that the motionMotion planner, which performs more time-consuming calculations, can succeed in computing the trajectories that achieve the corresponding goals. We propose an approach to solve the decision-making problem based on ideas from the referenceReference governorReference Governor (RG). Our method constructs backward reachableReachable sets for goals and collisionCollision areas for maneuvers that are generated by dynamical modelsModels parametrized by target values of vehicle motionMotion quantities. Online, theReference Governor (RG) referenceReference governorGovernor determines the existence of parameter values that provide membership of the state-parameter vector in a goal reachable set, and non-membership in all collision reachable sets. The resulting online computations are simple and fast, allowing solutionSolution of the decision-making process at higher rate and with minimal resources as required for standard automotive computing platforms. Furthermore, the method can provide referenceReference maneuvers to guide the motionMotion planningPlanning in determining the actual trajectory, can include robustness metrics, and is extended to handle uncertainty in the motionMotion of the obstacles to be avoided. We show simulation results in scenarios involving laneLane change, braking at intersections, and obstacles with changing velocity.