  It is important 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. This is a difficult
problem, especially in urban environments with interactions between many
vehicles. 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 which achieves these objectives. GRIT makes use of decision
trees trained on vehicle trajectory data. We evaluate GRIT on two datasets,
showing that GRIT achieved fast inference speed and comparable accuracy to two
deep learning baselines, a planning-based goal recognition method, and an
ablation of GRIT. We show that the learned trees are human interpretable and
demonstrate how properties of GRIT can be formally verified using a
satisfiability modulo theories (SMT) solver.
