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dc.contributor.authorNayak, Abhisheken
dc.contributor.authorRathinam, Sivakumaren
dc.contributor.authorGopalswamy, Swaminathanen
dc.date.accessioned2020-10-22T19:03:25Zen
dc.date.available2020-10-22T19:03:25Zen
dc.date.issued2020-06en
dc.identifier.urihttp://hdl.handle.net/10919/100646en
dc.description.abstractThe objective of this project was to explore how an autonomous vehicle identifies and safely responds to emergency vehicles using visual and other onboard sensors. Emergency vehicles can include police, fire, hospital and other responders’ vehicles. An autonomous vehicle in the presence of an emergency vehicle must have the ability to accurately sense its surroundings in real-time and be able to safely yield to the emergency vehicle. This project used machine learning algorithms to identify the presence of emergency vehicles, mainly through onboard vision, and then maneuver an in-path non-emergency autonomous vehicle to a stop on the curbside. Two image processing frameworks were tested to identify the best combination of vision-based detection algorithms, and a novel lateral control algorithm was developed for maneuvering the autonomous vehicle.en
dc.format.mimetypeapplication/pdfen
dc.language.isoen_USen
dc.publisherSAFE-D: Safety Through Disruption National UTCen
dc.relation.ispartofseriesSAFE-D;03-051en
dc.rightsCC0 1.0 Universalen
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/en
dc.subjecttransportation safetyen
dc.subjectemergency vehiclesen
dc.subjectautonomous vehiclesen
dc.subjectmachine visionen
dc.subjectmachine learningen
dc.titleResponse of Autonomous Vehicles to Emergency Response Vehicles (RAVEV)en
dc.typeReporten


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