Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention
Name
pa-mpc_final.pdf
Description
Main article
Size
1.13 MB
Format
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Author(s)
Schwarting, Wilko
Alonso Mora, Javier
Paull, Liam
Karaman, Sertac
Rus, Daniela L
Date Issued
September 2017
Journal
2017 IEEE International Conference Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Schwarting, Wilko; Alonso-Mora, Javier; Paull, Liam; Karaman, Sertac and Rus, Daniela. "Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention." 2017 IEEE International Conference Robotics and Automation (ICRA), May-June 2017, Singapore, Institute of Electrical and Electronics Engineers (IEEE), September 2017.
Version
Author's final manuscript
Abstract
Current state-of-the-art vehicle safety systems,
such as assistive braking or automatic lane following, are
still only able to help in relatively simple driving situations.
We introduce a Parallel Autonomy shared-control framework
that produces safe trajectories based on human inputs even in
much more complex driving scenarios, such as those commonly
encountered in an urban setting. We minimize the deviation
from the human inputs while ensuring safety via a set of
collision avoidance constraints. We develop a receding horizon
planner formulated as a Non-linear Model Predictive Control
(NMPC) including analytic descriptions of road boundaries,
and the configurations and future uncertainties of other traffic
participants, and directly supplying them to the optimizer
without linearization. The NMPC operates over
both steering and acceleration simultaneously. Furthermore, the proposed receding horizon planner also applies to fully autonomous vehicles. We validate the proposed approach through simulations
in a wide variety of complex driving scenarios such as left-
turns across traffic, passing on busy streets, and under dynamic
constraints in sharp turns on a race track.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
http://www.icra2017.org/conference/program
