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.

中文翻译：

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可正确校正自动驾驶的控制架构

本文提出了一种在确定性环境中用于全自动驾驶汽车的新型两级控制架构，该架构可以处理交通规则（如规范）和具有实时性能的低级车辆控制。在顶层，我们使用环境和车辆动力学的简单表示法来表达线性模型预测控制（MPC）问题。我们使用信号时序逻辑（STL）公式描述交通规则和安全约束，这些公式映射到优化问题中的混合整数线性约束。在顶层使用在顶层获得的解决方案来确定最佳控制命令，以便在更详细的框架中满足约束条件。在底层，基于规范的运行时监视技术，连同环境和车辆动力学的详细表示一起，用于补偿MPC中使用的简单模型与实际复杂模型之间的不匹配。在运行时性能的意义上，我们对文献中的现有方法进行了重大改进，并且在模拟器CARLA中验证了我们提出的控制方法的有效性。