Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplished through the application of causal discovery techniques. However, as it stands observational causal discovery techniques struggle to adequately cope with conditions such as causal sparsity and non-stationarity typically seen during online usage in autonomous agent domains. Meanwhile, interventional techniques are not always feasible due to domain restrictions. In order to better explore the issues facing observational techniques and promote further discussion of these topics we carry out a benchmark across 10 contemporary observational temporal causal discovery methods in the domain of autonomous driving. By evaluating these methods upon causal scenes drawn from real world datasets in addition to those generated synthetically we highlight where improvements need to be made in order to facilitate the application of causal discovery techniques to the aforementioned use-cases. Finally, we discuss potential directions for future work that could help better tackle the difficulties currently experienced by state of the art techniques.

翻译：自主机器人需要推理其环境中动态智能体的行为。通常通过应用因果发现技术来构建描述这些关系的模型。然而，目前基于观测的因果发现技术在应对自主智能体领域在线使用中常见的因果稀疏性和非平稳性等条件时存在困难。同时，由于领域限制，干预性技术并非总是可行。为了更深入地探讨观测技术面临的问题并促进对这些主题的进一步讨论，我们在自动驾驶领域对10种当代基于时间观测的因果发现方法进行了基准测试。通过在真实世界数据集和合成数据集生成的因果场景上评估这些方法，我们指出了需要改进之处，以便将因果发现技术应用于上述用例。最后，我们讨论了未来工作的潜在方向，以更好地解决当前最先进技术所面临的困难。