Much work has been done to develop causal reasoning techniques across a number of domains, however the utilisation of causality within autonomous systems is still in its infancy. Autonomous systems would greatly benefit from the integration of causality through the use of representations such as structural causal models (SCMs). The system would be afforded a higher level of transparency, it would enable post-hoc explanations of outcomes, and assist in the online inference of exogenous variables. These qualities are either directly beneficial to the autonomous system or a valuable step in building public trust and informing regulation. To such an end we present a case study in which we describe a module-based autonomous driving system comprised of SCMs. Approaching this task requires considerations of a number of challenges when dealing with a system of great complexity and size, that must operate for extended periods of time by itself. Here we describe these challenges, and present solutions. The first of these is SCM contexts, with the remainder being three new variable categories -- two of which are based upon functional programming monads. Finally, we conclude by presenting an example application of the causal capabilities of the autonomous driving system. In this example, we aim to attribute culpability between vehicular agents in a hypothetical road collision incident.

翻译：尽管跨多个领域的因果推理技术已取得大量研究成果，但因果关系在自主系统中的实际应用仍处于起步阶段。通过采用结构因果模型（SCMs）等表征形式整合因果关系，将显著提升自主系统的性能。该系统将获得更高水平的透明度，支持对结果进行事后解释，并辅助外生变量的在线推断。这些特性不仅直接有益于自主系统本身，更是建立公众信任和制定监管规范的关键步骤。为此，我们提出一个案例研究，描述了一个由SCMs构成的模块化自动驾驶系统。实现该任务需要综合考虑处理高度复杂庞大系统时面临的诸多挑战，这类系统需具备长期独立运行能力。本文系统阐述了这些挑战及其解决方案：首要方案是SCM上下文机制，其余方案涉及三个新型变量类别——其中两类基于函数式编程单子。最后，我们通过展示自动驾驶系统因果推理能力的应用实例进行总结。该实例旨在对假设道路碰撞事件中车辆智能体之间的责任归属进行归因分析。