Goal recognition (GR) allows the future behaviour of vehicles to be more accurately predicted. GR involves inferring the goals of other vehicles, such as a certain junction exit. In autonomous driving, vehicles can encounter many different scenarios and the environment is partially observable due to occlusions. We present a novel GR method named Goal Recognition with Interpretable Trees under Occlusion (OGRIT). We demonstrate that OGRIT can handle missing data due to occlusions and make inferences across multiple scenarios using the same learned decision trees, while still being fast, accurate, interpretable and verifiable. We also present the inDO and roundDO datasets of occluded regions used to evaluate OGRIT.