  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.
