标题： 扩展用于自动驾驶车辆的结构因果模型以简化时间系统构建并实现智能体之间的动态交互 显示英文标题

标题： Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents

摘要： 在本工作中，我们旨在弥合自动驾驶车辆与因果推理之间的差距。 自动驾驶车辆越来越频繁地与人类驾驶员互动，在许多情况下可能对与其互动的人的身体或心理健康构成风险。 同时，尽管因果模型具有内在的透明性并能够提供对比性解释，但它们在这些系统中的应用却十分有限。 因此，我们首先确定了限制结构因果模型在自动驾驶车辆中集成的挑战。 然后，我们引入了一些理论扩展，以应对这些挑战。 这增强了这些模型，使其具备更高的模块化和封装性，并展示了具有恒定空间复杂度的时间因果模型表示。 我们还通过所引入的扩展证明，动态可变集合（例如，随时间变化的自动驾驶车辆数量）可以在结构因果模型中使用，同时保持一种宽松形式的因果平稳性。 最后，我们讨论了这些扩展在自动驾驶车辆和服务机器人领域中的应用，以及未来工作的潜在方向。 显示英文摘要

摘要： In this work we aim to bridge the divide between autonomous vehicles and causal reasoning. Autonomous vehicles have come to increasingly interact with human drivers, and in many cases may pose risks to the physical or mental well-being of those they interact with. Meanwhile causal models, despite their inherent transparency and ability to offer contrastive explanations, have found limited usage within such systems. As such, we first identify the challenges that have limited the integration of structural causal models within autonomous vehicles. We then introduce a number of theoretical extensions to the structural causal model formalism in order to tackle these challenges. This augments these models to possess greater levels of modularisation and encapsulation, as well presenting temporal causal model representation with constant space complexity. We also prove through the extensions we have introduced that dynamically mutable sets (e.g. varying numbers of autonomous vehicles across time) can be used within a structural causal model while maintaining a relaxed form of causal stationarity. Finally we discuss the application of the extensions in the context of the autonomous vehicle and service robotics domain along with potential directions for future work.