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

Conference paper (2018)

Authors

Bingyu Zhou Student
Wilko Schwarting Massachusetts Institute of Technology
Daniela Rus Massachusetts Institute of Technology
J. Alonso-Mora Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering
Research Group
Learning & Autonomous Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2018 Bingyu Zhou, Wilko Schwarting, Daniela Rus, J. Alonso-Mora
DOI
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https://doi.org/10.1109/ICRA.2018.8461138
More Info
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Published Date

2018

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Learning & Autonomous Control

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.