A method for controlling an ego-vehicle in approaching intersections using a driver assistance system or an autonomous driving system, the method comprising steps of acquiring sensor data on an environment of the ego-vehicle from at least one sensor (S1), generating an environment representation based on the acquired sensor data (S 2 ), predicting at least one behavior of the ego-vehicle including at least one trajectory of the ego-vehicle (S8), determining at least one area in the environment of the ego-vehicle , wherein for the at least one area no sensor data is available (S3), generating at least one virtual traffic entity in the at least one determined area, wherein at least one behavior of the virtual traffic entity is predicted which interacts with the at least one predicted behavior of the ego-vehicle (S4, S5), calculating a risk measure for each combination of the at least one behavior of the virtual traffic entity and the predicted behavior of the ego-vehicle (S6), evaluating the calculated risk measure (S7), and executing a controlling action for the ego-vehicle based on the evaluated risk measure (S10), and characterized in that incoming lanes of the intersection are determined and only one virtual traffic entity located at a position closest to the intersection is generated for each relevant lane, wherein all incoming lanes, which have right of way priority over the ego-vehicle's lane, are selected as relevant lane, the calculated risk measure corresponds to a probability value of a critical event including one of a virtual collision or a virtual distance between the ego-vehicle and the at least one virtual traffic entity multiplied with a severity of consequences when the critical event occurs, and the severity of consequences includes at least one of a relative collision speed, collision directions, impact points at the time of the collision and vehicle masses of the ego-vehicle and the at least one virtual traffic entity . acquiring sensor data on an environment of the ego-vehicle from at least one sensor (S1), generating an environment representation based on the acquired sensor data (S 2 ), predicting at least one behavior of the ego-vehicle including at least one trajectory of the ego-vehicle (S8), determining at least one area in the environment of the ego-vehicle , wherein for the at least one area no sensor data is available (S3), generating at least one virtual traffic entity in the at least one determined area, wherein at least one behavior of the virtual traffic entity is predicted which interacts with the at least one predicted behavior of the ego-vehicle (S4, S5), calculating a risk measure for each combination of the at least one behavior of the virtual traffic entity and the predicted behavior of the ego-vehicle (S6), evaluating the calculated risk measure (S7), and executing a controlling action for the ego-vehicle based on the evaluated risk measure (S10), and incoming lanes of the intersection are determined and only one virtual traffic entity located at a position closest to the intersection is generated for each relevant lane, wherein all incoming lanes, which have right of way priority over the ego-vehicle's lane, are selected as relevant lane, the calculated risk measure corresponds to a probability value of a critical event including one of a virtual collision or a virtual distance between the ego-vehicle and the at least one virtual traffic entity multiplied with a severity of consequences when the critical event occurs, and the severity of consequences includes at least one of a relative collision speed, collision directions, impact points at the time of the collision and vehicle masses of the ego-vehicle and the at least one virtual traffic entity . <EOS>