Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving

Bingyu Zhou; Wilko Schwarting; Daniela Rus; Javier Alonso-Mora

doi:10.1109/ICRA.2018.8461138

Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving

Bingyu Zhou, Wilko Schwarting, Daniela Rus, Javier Alonso-Mora

Learning & Autonomous Control

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

28 Citations (Scopus)

160 Downloads (Pure)

Abstract

When driving in urban environments, an autonomous vehicle must account for the interaction with other traffic participants. It must reason about their future behavior, how its actions affect their future behavior, and potentially
consider multiple motion hypothesis. In this paper we introduce a method for joint behavior estimation and trajectory planning that models interaction and multi-policy decisionmaking. The method leverages Partially Observable Markov Decision Processes to estimate the behavior of other traffic participants given the planned trajectory for the ego-vehicle, and Receding-Horizon Control for generating safe trajectories for the ego-vehicle. To achieve safe navigation we introduce chance constraints over multiple motion policies in the recedinghorizon planner. These constraints account for uncertainty over
the behavior of other traffic participants. The method is capable of running in real-time and we show its performance and good scalability in simulated multi-vehicle intersection scenarios.

Original language	English
Title of host publication	Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018)
Editors	Kevin Lynch
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Pages	2388-2394
ISBN (Electronic)	978-1-5386-3081-5
DOIs	https://doi.org/10.1109/ICRA.2018.8461138
Publication status	Published - 2018
Event	ICRA 2018: 2018 IEEE International Conference on Robotics and Automation - Brisbane Convention & Exhibition Centre, Brisbane, Australia Duration: 21 May 2018 → 25 May 2018

Conference

Conference	ICRA 2018: 2018 IEEE International Conference on Robotics and Automation
Country/Territory	Australia
City	Brisbane
Period	21/05/18 → 25/05/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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Joint_Multi-Policy_Behavior_Estimation_and_Receding-Horizon_Trajectory_Planning_for_Automated_Urban_DrivingFinal published version, 1.96 MB

Cite this

@inproceedings{9ce9e93c06b043c993b73f6d66085a1a,

title = "Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving",

abstract = "When driving in urban environments, an autonomous vehicle must account for the interaction with other traffic participants. It must reason about their future behavior, how its actions affect their future behavior, and potentiallyconsider multiple motion hypothesis. In this paper we introduce a method for joint behavior estimation and trajectory planning that models interaction and multi-policy decisionmaking. The method leverages Partially Observable Markov Decision Processes to estimate the behavior of other traffic participants given the planned trajectory for the ego-vehicle, and Receding-Horizon Control for generating safe trajectories for the ego-vehicle. To achieve safe navigation we introduce chance constraints over multiple motion policies in the recedinghorizon planner. These constraints account for uncertainty overthe behavior of other traffic participants. The method is capable of running in real-time and we show its performance and good scalability in simulated multi-vehicle intersection scenarios.",

author = "Bingyu Zhou and Wilko Schwarting and Daniela Rus and Javier Alonso-Mora",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; ICRA 2018: 2018 IEEE International Conference on Robotics and Automation ; Conference date: 21-05-2018 Through 25-05-2018",

year = "2018",

doi = "10.1109/ICRA.2018.8461138",

language = "English",

pages = "2388--2394",

editor = "Kevin Lynch",

booktitle = "Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018)",

publisher = "IEEE",

address = "United States",

}

Zhou, B, Schwarting, W, Rus, D & Alonso-Mora, J 2018, Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving. in K Lynch (ed.), Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018). IEEE, Piscataway, NJ, USA, pp. 2388-2394, ICRA 2018: 2018 IEEE International Conference on Robotics and Automation , Brisbane, Australia, 21/05/18. https://doi.org/10.1109/ICRA.2018.8461138

Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving. / Zhou, Bingyu; Schwarting, Wilko; Rus, Daniela et al.
Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2018). ed. / Kevin Lynch. Piscataway, NJ, USA: IEEE, 2018. p. 2388-2394.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Schwarting, Wilko

AU - Rus, Daniela

AU - Alonso-Mora, Javier

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2018

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AB - When driving in urban environments, an autonomous vehicle must account for the interaction with other traffic participants. It must reason about their future behavior, how its actions affect their future behavior, and potentiallyconsider multiple motion hypothesis. In this paper we introduce a method for joint behavior estimation and trajectory planning that models interaction and multi-policy decisionmaking. The method leverages Partially Observable Markov Decision Processes to estimate the behavior of other traffic participants given the planned trajectory for the ego-vehicle, and Receding-Horizon Control for generating safe trajectories for the ego-vehicle. To achieve safe navigation we introduce chance constraints over multiple motion policies in the recedinghorizon planner. These constraints account for uncertainty overthe behavior of other traffic participants. The method is capable of running in real-time and we show its performance and good scalability in simulated multi-vehicle intersection scenarios.

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ER -

Joint multi-policy behavior estimation and receding-horizon trajectory planning for automated urban driving

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