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Goal recognition (GR) involves inferring the goals of other vehicles, such asa certain junction exit, which can enable more accurate prediction of theirfuture behaviour. In autonomous driving, vehicles can encounter many differentscenarios and the environment may be partially observable due to occlusions. Wepresent a novel GR method named Goal Recognition with Interpretable Trees underOcclusion (OGRIT). OGRIT uses decision trees learned from vehicle trajectorydata to infer the probabilities of a set of generated goals. We demonstratethat OGRIT can handle missing data due to occlusions and make inferences acrossmultiple scenarios using the same learned decision trees, while beingcomputationally fast, accurate, interpretable and verifiable. We also releasethe inDO, rounDO and OpenDDO datasets of occluded regions used to evaluateOGRIT.

Verifiable Goal Recognition for Autonomous Driving with Occlusions