  Goal recognition (GR) involves inferring the goals of other vehicles, such as
a certain junction exit, which can enable more accurate prediction of their
future behaviour. In autonomous driving, vehicles can encounter many different
scenarios and the environment may be partially observable due to occlusions. We
present a novel GR method named Goal Recognition with Interpretable Trees under
Occlusion (OGRIT). OGRIT uses decision trees learned from vehicle trajectory
data to infer the probabilities of a set of generated goals. We demonstrate
that OGRIT can handle missing data due to occlusions and make inferences across
multiple scenarios using the same learned decision trees, while being
computationally fast, accurate, interpretable and verifiable. We also release
the inDO, rounDO and OpenDDO datasets of occluded regions used to evaluate
OGRIT.
