The development of automated vehicles brings new challenges to road safety. The behavior of the automated vehicles should be carefully designed in order to interact with the environment and other vehicles efficiently and safely. In our previous work, the authors proposed the robustly-safe automated driving system (ROAD) in order for the automated vehicle to prevent or minimize occurrences of collisions with surrounding vehicles and moving objects while maintaining efficiency. In this paper, a set of design principles are elaborated based on our previous work, including robust perception and cognition algorithms for environment monitoring and high level decision making and low level control algorithms for safe maneuvering of the vehicles. The autonomous driving problem in mixed traffic is posed as a stochastic optimization problem, which is solved by 1) behavior classification and trajectory prediction of the surrounding vehicles, and 2) an unique parallel planner architecture which addresses the efficiency goal and the safety goal separately. Extensive simulations are performed to validate the effectiveness of the proposed algorithm, which evaluate both high level decision making and low level vehicle regulation. Two typical scenarios are considered, driving on freeway and driving in unstructured environments such as parking lots. In the simulation, multiple moving agents representing surrounding vehicles and pedestrians are added to the environment, some of which are controlled by human users in order to test the real time response of the automated vehicle.