  This paper presents a novel two-level control architecture for a fully
autonomous vehicle in a deterministic environment, which can handle traffic
rules as specifications and low-level vehicle control with real-time
performance. At the top level, we use a simple representation of the
environment and vehicle dynamics to formulate a linear Model Predictive Control
(MPC) problem. We describe the traffic rules and safety constraints using
Signal Temporal Logic (STL) formulas, which are mapped to mixed integer-linear
constraints in the optimization problem. The solution obtained at the top level
is used at the bottom-level to determine the best control command for
satisfying the constraints in a more detailed framework. At the bottom-level,
specification-based runtime monitoring techniques, together with detailed
representations of the environment and vehicle dynamics, are used to compensate
for the mismatch between the simple models used in the MPC and the real complex
models. We obtain substantial improvements over existing approaches in the
literature in the sense of runtime performance and we validate the
effectiveness of our proposed control approach in the simulator CARLA.
