Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention
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 traject...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/110365 https://orcid.org/0000-0003-0058-570X https://orcid.org/0000-0003-2492-6660 https://orcid.org/0000-0002-2225-7275 https://orcid.org/0000-0001-5473-3566 |
| Summary: | 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. |
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