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.
翻译:本文介绍了在确定性环境下完全自主的车辆的新型双层控制结构,这种结构可以将交通规则作为规格和低级别车辆实时性能控制处理,在顶层一级,我们使用简单的环境和车辆动态说明来制定线性模型预测控制(MPC)问题。我们用信号时空逻辑(STL)公式描述交通规则和安全限制,这些公式在优化问题的整数-线性制约下绘制图。在顶层获得的解决方案在底层用于确定最佳控制指令,以在更详细的框架内满足这些限制。在底层,基于规格的运行时间监测技术,加上环境和车辆动态的详细描述,用来弥补在MPC使用的简单模型与实际复杂模型之间的不匹配。我们从运行性能的意义上对文献中的现有方法进行了重大改进,我们验证了我们在模拟CARLA中拟议的控制方法的有效性。