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

Conference Paper (2018)
Author(s)

Bingyu Zhou (Student TU Delft)

Wilko Schwarting (Massachusetts Institute of Technology)

Daniela Rus (Massachusetts Institute of Technology)

J. Alonso-Mora (TU Delft - Learning & Autonomous Control)

Research Group
Learning & Autonomous Control
Copyright
© 2018 Bingyu Zhou, Wilko Schwarting, Daniela Rus, J. Alonso-Mora
DOI related publication
https://doi.org/10.1109/ICRA.2018.8461138
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Bingyu Zhou, Wilko Schwarting, Daniela Rus, J. Alonso-Mora
Research Group
Learning & Autonomous Control
Pages (from-to)
2388-2394
ISBN (electronic)
978-1-5386-3081-5
Reuse Rights

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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.

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