Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents. Despite this, the state of the art struggles to meet the need of agents to discover causal links between themselves and others. Observational approaches struggle because of the non-stationarity of causal links in dynamic environments, and the sparsity of causal interactions while requiring the approaches to work in an online fashion. Meanwhile interventional approaches are impractical as a vehicle cannot experiment with its actions on a public road. To counter the issue of non-stationarity we reformulate the problem in terms of extracted events, while the previously mentioned restriction upon interventions can be overcome with the use of counterfactual simulation. We present three variants of the proposed counterfactual causal discovery method and evaluate these against state of the art observational temporal causal discovery methods across 3396 causal scenes extracted from a real world driving dataset. We find that the proposed method significantly outperforms the state of the art on the proposed task quantitatively and can offer additional insights by comparing the outcome of an alternate series of decisions in a way that observational and interventional approaches cannot.

翻译：智能驾驶智能体需具备推演自身行为如何影响其他主体行为的能力，这是其核心技能之一。然而，现有技术在处理智能体间因果关联发现方面仍存在不足。观测学习方法因动态环境中因果链的非平稳性及因果交互的稀疏性而面临挑战，且需以在线方式运行；而干预式方法难以在公共道路上对车辆行为进行实验操作。针对非平稳性问题，我们将因果发现任务重构为基于事件提取的框架；同时，通过反事实仿真克服干预方法的局限性。本文提出三种反事实因果发现方法变体，并基于真实驾驶数据集提取的3396个因果场景，与现有最优的时序观测因果发现方法进行对比评估。结果表明，所提方法在定量指标上显著超越现有最优水平，并能通过比较备选决策序列的仿真结果提供额外洞见——这是观测与干预方法所无法实现的。