This paper studies a real-time and scalable decision and control method with safety assurance for automated vehicles. It also develops theory for mixed-driven reinforcement learning (RL) with high learning efficiency and performance, and builds a distributed asynchronous parallel computing toolchain for solving the driving policy. This work lays the foundation for the decision and control functionality of high-level automated vehicles.