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dc.contributor.authorOrt, Moses Teddy
dc.contributor.authorPierson, Alyssa
dc.contributor.authorGilitschenski, Igor
dc.contributor.authorAraki, Brandon
dc.contributor.authorKaraman, Sertac
dc.contributor.authorRus, Daniela L
dc.contributor.authorLeonard, John J
dc.date.accessioned2020-08-12T15:41:30Z
dc.date.available2020-08-12T15:41:30Z
dc.date.issued2019-07
dc.date.submitted2019-05
dc.identifier.issn2377-3774
dc.identifier.urihttps://hdl.handle.net/1721.1/126542
dc.description.abstractAmong traffic accidents in the USA, 23% of fatal and 32% of non-fatal incidents occurred at intersections. For driver assistance systems, intersection navigation remains a difficult problem that is critically important to increasing driver safety. In this letter, we examine how to navigate an unsignalized intersection safely under occlusions and faulty perception. We propose a real-time, probabilistic, risk assessment for parallel autonomy control applications for occluded intersection scenarios. The algorithms are implemented on real hardware and are deployed in a variety of turning and merging topologies. We show phenomena that establish go/no-go decisions, augment acceleration through an intersection and encourage nudging behaviors toward intersections.en_US
dc.description.sponsorshipUnited States. Office of Naval Research (Grant N00014-18-1-2830)en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/LRA.2019.2931823en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceMIT web domainen_US
dc.titleProbabilistic Risk Metrics for Navigating Occluded Intersectionsen_US
dc.typeArticleen_US
dc.identifier.citationMcGill, Stephen G. et al. “Probabilistic Risk Metrics for Navigating Occluded Intersections.” IEEE robotics and automation letters, vol. 4, no. 4, 2019, pp. 4322 - 4329 © 2019 The Author(s)en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.relation.journalIEEE robotics and automation lettersen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2019-10-29T16:16:57Z
dspace.date.submission2019-10-29T16:17:04Z
mit.journal.volume4en_US
mit.journal.issue4en_US


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