We propose a method of classifying the operation of a system into finitely many modes. Each mode has its own objectives for the system's behaviour and its own mathematical models and algorithms designed to accomplish its objectives. A central problem is deciding when to transition from one mode to some other mode, a decision that may be contested and involve partial or inconsistent information or evidence. We model formally the concept of modes for a system and derive a family of data types for analysing mode transitions. The data types are simplicial complexes, both abstract and realised in euclidean space $\mathbb{R}^{n}$. In the data type, a mode is represented by a simplex. Each state of a system can be evaluated relative to different modes by mapping it into one or more simplices. This calibration measures the extent to which distinct modes are appropriate for the state and can decide on a transition. We explain this methodology based on modes, introduce the mathematical ideas about simplicial objects we need and use them to build a theoretical framework for modes and mode transitions. To illustrate the general model in some detail, we work though a case study of an autonomous racing car.
翻译:我们建议一种方法,将一个系统的运行分为有限的多种模式。每种模式都有系统行为和数学模型和算法的自身目标,目的是实现其目标。一个中心问题是决定何时从一个模式过渡到其他模式,这一决定可能会引起争议,并涉及部分或不一致的信息或证据。我们正式模拟一个系统的模式概念,并产生分析模式过渡的数据类型组合。数据类型是简单复杂的,既包括抽象的,也包括在eumclidean空间中实现的。在数据类型中,一种模式由简单x代表。一个系统的每个状态可以通过将其映射成一个或一个以上简易模式来评估与不同模式的相对。这种校准衡量不同模式适合州的程度,并可以决定过渡。我们根据模式解释这一方法,引入关于简单对象的数学概念,我们需要用它们来构建一个模式和模式转型的理论框架。为了较详细地说明一般模式,我们通过对自主汽车进行案例研究来评估。