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dc.contributor.authorPendelton, Scott Drew
dc.contributor.authorAndersen, Hans
dc.contributor.authorAng, Marcelo H., Jr.
dc.contributor.authorNaser, Felix
dc.contributor.authorDorhout, David Lee
dc.contributor.authorProulx, Stephen
dc.contributor.authorSchwarting, Wilko
dc.contributor.authorPaull, Liam
dc.contributor.authorAlonso Mora, Javier
dc.contributor.authorKaraman, Sertac
dc.contributor.authorTedrake, Russell L
dc.contributor.authorLeonard, John J
dc.contributor.authorRus, Daniela L
dc.date.accessioned2017-06-27T18:27:44Z
dc.date.available2017-06-27T18:27:44Z
dc.date.issued2017-06
dc.identifier.urihttp://hdl.handle.net/1721.1/110322
dc.description.abstractWe present the development of a full-scale “parallel autonomy” research platform including software and hardware. In the parallel autonomy paradigm, the control of the vehicle is shared; the human is still in control of the vehicle, but the autonomy system is always running in the background to prevent accidents. Our holistic approach includes: (1) a driveby-wire conversion method only based on reverse engineering, (2) mounting of relatively inexpensive sensors onto the vehicle, (3) implementation of a localization and mapping system, (4) obstacle detection and (5) a shared controller as well as (6) integration with an advanced autonomy simulation system (Drake) for rapid development and testing. The system can operate in three modes: (a) manual driving, (b) full autonomy, where the system is in complete control of the vehicle and (c) parallel autonomy, where the shared controller is implemented. We present results from extensive testing of a full-scale vehicle on closed tracks that demonstrate these capabilities.en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttps://its.papercept.net/conferences/conferences/IV2017/program/IV2017_ContentListWeb_3.html#tubt4_04en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceRusen_US
dc.titleA Parallel Autonomy Research Platformen_US
dc.typeArticleen_US
dc.identifier.citationNaser, Felix et al. "A Parallel Autonomy Research Platform." 2017 IEEE Intelligent Vehicles Symposium. 11-14 June, 2017, Redondo Beach, CA, USA, IEEE, 2017.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.approverRus, Danielaen_US
dc.contributor.mitauthorNaser, Felix
dc.contributor.mitauthorDorhout, David Lee
dc.contributor.mitauthorProulx, Stephen
dc.contributor.mitauthorSchwarting, Wilko
dc.contributor.mitauthorPaull, Liam
dc.contributor.mitauthorAlonso Mora, Javier
dc.contributor.mitauthorKaraman, Sertac
dc.contributor.mitauthorTedrake, Russell L
dc.contributor.mitauthorLeonard, John J
dc.contributor.mitauthorRus, Daniela L
dc.relation.journal2017 IEEE Intelligent Vehicles Symposiumen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dspace.orderedauthorsNaser, Felix; Dorhout, David; Proulx, Stephen; Pendleton, Scott Drew; Andersen, Hans; Schwaring, Wilko; Paull, Liam; Alonso-Mora, Javier; Ang, Marcelo H., Jr.; Karaman, Sertac; Tedrake, Russ; Leonard, John; Rus, Danielaen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-2752-2311
dc.identifier.orcidhttps://orcid.org/0000-0003-2492-6660
dc.identifier.orcidhttps://orcid.org/0000-0003-0058-570X
dc.identifier.orcidhttps://orcid.org/0000-0002-2225-7275
dc.identifier.orcidhttps://orcid.org/0000-0002-8712-7092
dc.identifier.orcidhttps://orcid.org/0000-0002-8863-6550
dc.identifier.orcidhttps://orcid.org/0000-0001-5473-3566
mit.licenseOPEN_ACCESS_POLICYen_US


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