  We propose an integrated prediction and planning system for autonomous
driving which uses rational inverse planning to recognise the goals of other
vehicles. Goal recognition informs a Monte Carlo Tree Search (MCTS) algorithm
to plan optimal maneuvers for the ego vehicle. Inverse planning and MCTS
utilise a shared set of defined maneuvers and macro actions to construct plans
which are explainable by means of rationality principles. Evaluation in
simulations of urban driving scenarios demonstrate the system's ability to
robustly recognise the goals of other vehicles, enabling our vehicle to exploit
non-trivial opportunities to significantly reduce driving times. In each
scenario, we extract intuitive explanations for the predictions which justify
the system's decisions.

Robotics