  It is useful for autonomous vehicles to have the ability to infer the goals
of other vehicles (goal recognition), in order to safely interact with other
vehicles and predict their future trajectories. Goal recognition methods must
be fast to run in real time and make accurate inferences. As autonomous driving
is safety-critical, it is important to have methods which are human
interpretable and for which safety can be formally verified. Existing goal
recognition methods for autonomous vehicles fail to satisfy all four objectives
of being fast, accurate, interpretable and verifiable. We propose Goal
Recognition with Interpretable Trees (GRIT), a goal recognition system for
autonomous vehicles which achieves these objectives. GRIT makes use of decision
trees trained on vehicle trajectory data. Evaluation on two vehicle trajectory
datasets demonstrates the inference speed and accuracy of GRIT compared to an
ablation and two deep learning baselines. We show that the learned trees are
human interpretable and demonstrate how properties of GRIT can be formally
verified using an SMT solver.
