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：能够推理自身行为如何影响他人行为，是智能驾驶智能体所需的核心技能。然而，现有方法难以满足智能体发现自身与他人之间因果关联的需求。观测性方法面临动态环境中因果链接的非平稳性、因果交互的稀疏性，以及需在线运行的要求等挑战。而干预性方法因车辆无法在公共道路上进行行为实验而不具备可行性。为应对非平稳性问题，我们基于提取的事件重新定义问题；同时，通过反事实仿真克服前述对干预方法的限制。我们提出了三种反事实因果发现方法的变体，并在从真实世界驾驶数据集中提取的3396个因果场景中，将其与当前最先进的观测性时序因果发现方法进行对比评估。实验结果表明，所提方法在定量指标上显著优于现有方法，并能通过比较替代决策序列的结果提供额外洞见——这是观测性和干预性方法无法实现的。