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: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2017
|
| 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 |
| _version_ | 1826213007175188480 |
|---|---|
| author | Schwarting, Wilko Alonso Mora, Javier Paull, Liam Karaman, Sertac Rus, Daniela L |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Schwarting, Wilko Alonso Mora, Javier Paull, Liam Karaman, Sertac Rus, Daniela L |
| author_sort | Schwarting, Wilko |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:41:37Z |
| format | Article |
| id | mit-1721.1/110365 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:41:37Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1103652022-09-29T15:35:49Z Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention Schwarting, Wilko Alonso Mora, Javier Paull, Liam Karaman, Sertac Rus, Daniela L Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Laboratory for Information and Decision Systems Schwarting, Wilko Schwarting, Wilko Alonso Mora, Javier Paull, Liam Karaman, Sertac Rus, Daniela L 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. 2017-06-28T20:28:36Z 2017-06-28T20:28:36Z 2017-09 2017-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-5090-4633-1 978-1-5090-4632-4 http://hdl.handle.net/1721.1/110365 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. 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 en_US http://www.icra2017.org/conference/program 2017 IEEE International Conference Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Schwarting |
| spellingShingle | Schwarting, Wilko Alonso Mora, Javier Paull, Liam Karaman, Sertac Rus, Daniela L Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention |
| title | Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention |
| title_full | Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention |
| title_fullStr | Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention |
| title_full_unstemmed | Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention |
| title_short | Parallel Autonomy in Automated Vehicles: Safe Motion Generation with Minimal Intervention |
| title_sort | parallel autonomy in automated vehicles safe motion generation with minimal intervention |
| url | 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 |
| work_keys_str_mv | AT schwartingwilko parallelautonomyinautomatedvehiclessafemotiongenerationwithminimalintervention AT alonsomorajavier parallelautonomyinautomatedvehiclessafemotiongenerationwithminimalintervention AT paullliam parallelautonomyinautomatedvehiclessafemotiongenerationwithminimalintervention AT karamansertac parallelautonomyinautomatedvehiclessafemotiongenerationwithminimalintervention AT rusdanielal parallelautonomyinautomatedvehiclessafemotiongenerationwithminimalintervention |