We propose a two-level hierarchical architecture for controlling an autonomous vehicle (ego) in complex urban driving environments. This approach ensures both collision avoidance and adherence to traffic rules while maintaining real-time performance. At the top level of our framework, we use a simple dynamic model for ego and a simplified representation of the environment to formulate a Model Predictive Control (MPC) problem. The traffic rules are represented by Signal Temporal Logic (STL) formulas and incorporated as mixed integer-linear constraints within the MPC optimization. The top level MPC solution is then simulated at the bottom level, which employs detailed models of both ego dynamics and the environment. If a collision or traffic rule violation occurs, the bottom level provides feedback to the top level in the form of correction constraints, which are mixed integer-linear constraints affecting the state and control input of ego. This closed-loop feedback from the bottom level helps address discrepancies between the simplified models used in the MPC and the complex real-world models. We assess the effectiveness and runtime performance of our method by comparing it with existing approaches, through simulations of various urban driving scenarios in the CARLA simulator.