Joint Multi-Policy Behavior Estimation and Receding-Horizon Trajectory Planning for Automated Urban Driving
Name
18-zhou-ICRA.pdf
Description
Accepted version
Size
803.4 KB
Format
Adobe PDF
Checksum (MD5)
636eaeeac6207609ddd7d2ab89331172
Author(s)
Zhou, Bingyu
Schwarting, Wilko
Rus, Daniela L
Alonso-Mora, Javier
Date Issued
September 2018
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Zhou, Bingyu et al. "Joint Multi-Policy Behavior Estimation and Receding-Horizon Trajectory Planning for Automated Urban Driving." IEEE International Conference on Robotics and Automation, May 2018, Brisbane, Australia, Institute of Electrical and Electronics Engineers, September 2018. © 2018 IEEE
Version
Author's final manuscript
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 decision-making. 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 receding-horizon 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.
MIT Department
MIT Schwarzmann College of Computing
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8461138
