Interpretable Goal-based Prediction and Planning for Autonomous Driving

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1043-1049
Number of pages7
ISBN (Electronic)978-1-7281-9077-8
ISBN (Print)978-1-7281-9078-5
DOIs
Publication statusPublished - 18 Oct 2021
Event2021 IEEE International Conference on Robotics and Automation - Xi'an, China
Duration: 30 May 20215 Jun 2021
http://www.icra2021.org/

Publication series

Name
ISSN (Print)1050-4729
ISSN (Electronic)2577-087X

Conference

Conference2021 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2021
Country/TerritoryChina
CityXi'an
Period30/05/215/06/21
Internet address

Fingerprint

Dive into the research topics of 'Interpretable Goal-based Prediction and Planning for Autonomous Driving'. Together they form a unique fingerprint.

Cite this