Perception, localization, planning, and control, high-level functions often organized in a so-called pipeline, are amongst the core building blocks of modern autonomous (ground, air, and underwater) vehicle architectures. These functions are increasingly being implemented using learning-enabled components (LECs), i.e., (software) components leveraging knowledge acquisition and learning processes such as deep learning. Providing quantified component-level assurance as part of a wider (dynamic) assurance case can be useful in supporting both pre-operational approval of LECs (e.g., by regulators), and runtime hazard mitigation, e.g., using assurance-based failover configurations. This paper develops a notion of assurance for LECs based on i) identifying the relevant dependability attributes, and ii) quantifying those attributes and the associated uncertainty, using probabilistic techniques. We give a practical grounding for our work using an example from the aviation domain: an autonomous taxiing capability for an unmanned aircraft system (UAS), focusing on the application of LECs as sensors in the perception function. We identify the applicable quantitative measures of assurance, and characterize the associated uncertainty using a non-parametric Bayesian approach, namely Gaussian process regression. We additionally discuss the relevance and contribution of LEC assurance to system-level assurance, the generalizability of our approach, and the associated challenges.
翻译:认知、本地化、规划和控制,往往是在所谓的管道中组织的高级功能,是现代自主(地面、空中和水下)车辆结构的核心构件之一,这些功能正越来越多地使用学习驱动组件(LECs),即(软件)组成部分,利用知识获取和学习过程,例如深层学习,来利用知识获取和学习过程;提供量化的组成部分保障,作为更广泛(动态)保证案例的一部分,可有助于支持LECs(例如由监管者)的操作前核准,以及运行时间风险缓解,例如,使用基于保证的错失配置。本文件为LECs制定了一种保证概念,其基础是:确定相关的可靠性属性属性,二)利用概率技术量化这些属性和相关不确定性。我们以航空领域为例,为我们的工作提供了实际的基础:无人驾驶飞机系统(UAS)的自主出租车能力,重点是将LECs用作感知功能中的传感器,重点是将LECs用作感测功能中的传感器。我们确定可适用的数量计量措施,即确定相关的可靠性,并用与LEC相关的稳定性相关的不确定性,我们用一种不可靠的方法来分析。