Autonomous Driving with Interpretable Goal Recognition and Monte Carlo Tree Search

Cillian Brewitt, Stefano V Albrecht, John Wilhelm, Balint Gyevnar, Francisco Eiras, Mihai Dobre, Ram Ramamoorthy

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

Abstract—The ability to predict the intentions and driving trajectories of other vehicles is a key problem for autonomous driving. We propose an integrated planning and prediction system which leverages the computational benefit of using a finite space of maneuvers, and extend the approach to planning and prediction of sequences of maneuvers via 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. Our system constructs plans which are explainable by means of rationality. Evaluation in simulations of four urban driving scenarios demonstrate the system’s ability to robustly recognise the goals of other vehicles while generating near-optimal plans.
Original languageEnglish
Number of pages6
Publication statusE-pub ahead of print - 13 Jul 2020
EventInteraction and Decision-Making in Autonomous-Driving: A Virtual Workshop at RSS 2020 -
Duration: 13 Jul 202013 Jul 2020


WorkshopInteraction and Decision-Making in Autonomous-Driving
Abbreviated titleIDA 2020
Internet address


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