This paper represents an automated driving control algorithm in urban traffic situation. In order to achieve a development of a highly automated driving control algorithm in urban environments, the research issues can be classified into two things. One of the issues is to determine a safe driving envelope with the consideration of probable risks and the other is to achieve robustness of control performance under disturbances and model uncertainties. While human drivers maneuver a vehicle, they determine appropriate steering angle and acceleration based on the predictable trajectories of the surrounding vehicles. Therefore, not only current states of surrounding vehicles but also predictable behaviors of surrounding vehicles and potential obstacles should be considered in designing an automated driving control algorithm. In order to analyze the probabilistic behaviors of surrounding vehicles, we collected driving data on a real road. Then, in order to guarantee safety to the possible change of traffic situation surrounding the subject vehicle during a finite time-horizon, the safe driving envelope which describes the safe driving condition over a finite time horizon is defined in consideration of probabilistic prediction of future positions of surrounding vehicles and potential obstacles. Since an automated driving control algorithm is required to operate in a wide operating region and limit the set of permissible states and inputs, a model predictive control (MPC) approach has been used widely in designing an automated driving control algorithm. MPC approach uses a dynamic model of the vehicle to predict the future states of the system and determines optimal control sequences at each time step to minimize a performance index while satisfying constraints based on the predicted future states. Since the solving nonlinear optimization problem has computational burden, we design an architecture which decides a desired steering angle and longitudinal acceleration parallel to reduce the computational load. For the guarantee of the robustness of control performance, a robust invariant set is used to ensure robust satisfaction of vehicle states and constraints against disturbances and model uncertainties. The effectiveness of the proposed control algorithm is evaluated by comparing between human driver data and proposed algorithm.