Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/151323
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DC FieldValueLanguage
dc.contributor.authorRuipeng Gaoen_US
dc.contributor.authorMingmin Zhaoen_US
dc.contributor.authorTao Yeen_US
dc.contributor.authorFan Yeen_US
dc.contributor.authorYizhou Wangen_US
dc.contributor.authorGuojie Luoen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:14:40Z-
dc.date.available2020-04-06T07:14:40Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMC.2017.2684167en_US
dc.identifier.urihttp://localhost/handle/Hannan/151323-
dc.description.abstractAlthough location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return. In this paper, we propose VeTrack, asmartphone-only system that tracks the vehicle&x2019;s location in real time using the phone&x2019;s inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel &x201C;shadow&x201D; trajectory tracing method to accurately estimate phone&x2019;s and vehicle&x2019;s orientations despite their arbitrary poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides, and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, and collect data with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle&x2019;s real time location with almost negligible latency, with error of <inline-formula><tex-math notation="LaTeX">2\sim 4</tex-math> <alternatives><inline-graphic xlink:href="gao-ieq1-2684167.gif"/></alternatives></inline-formula> parking spaces at the 80th percentile.en_US
dc.format.extent2023,en_US
dc.format.extent2036en_US
dc.publisherIEEEen_US
dc.relation.haspart7880567.pdfen_US
dc.titleSmartphone-Based Real Time Vehicle Tracking in Indoor Parking Structuresen_US
dc.typeArticleen_US
dc.journal.volume16en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
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7880567.pdf1.57 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRuipeng Gaoen_US
dc.contributor.authorMingmin Zhaoen_US
dc.contributor.authorTao Yeen_US
dc.contributor.authorFan Yeen_US
dc.contributor.authorYizhou Wangen_US
dc.contributor.authorGuojie Luoen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:14:40Z-
dc.date.available2020-04-06T07:14:40Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMC.2017.2684167en_US
dc.identifier.urihttp://localhost/handle/Hannan/151323-
dc.description.abstractAlthough location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return. In this paper, we propose VeTrack, asmartphone-only system that tracks the vehicle&x2019;s location in real time using the phone&x2019;s inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel &x201C;shadow&x201D; trajectory tracing method to accurately estimate phone&x2019;s and vehicle&x2019;s orientations despite their arbitrary poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides, and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, and collect data with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle&x2019;s real time location with almost negligible latency, with error of <inline-formula><tex-math notation="LaTeX">2\sim 4</tex-math> <alternatives><inline-graphic xlink:href="gao-ieq1-2684167.gif"/></alternatives></inline-formula> parking spaces at the 80th percentile.en_US
dc.format.extent2023,en_US
dc.format.extent2036en_US
dc.publisherIEEEen_US
dc.relation.haspart7880567.pdfen_US
dc.titleSmartphone-Based Real Time Vehicle Tracking in Indoor Parking Structuresen_US
dc.typeArticleen_US
dc.journal.volume16en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7880567.pdf1.57 MBAdobe PDF
Full metadata record
DC FieldValueLanguage
dc.contributor.authorRuipeng Gaoen_US
dc.contributor.authorMingmin Zhaoen_US
dc.contributor.authorTao Yeen_US
dc.contributor.authorFan Yeen_US
dc.contributor.authorYizhou Wangen_US
dc.contributor.authorGuojie Luoen_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-04-06T07:14:40Z-
dc.date.available2020-04-06T07:14:40Z-
dc.date.issued2017en_US
dc.identifier.other10.1109/TMC.2017.2684167en_US
dc.identifier.urihttp://localhost/handle/Hannan/151323-
dc.description.abstractAlthough location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return. In this paper, we propose VeTrack, asmartphone-only system that tracks the vehicle&x2019;s location in real time using the phone&x2019;s inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel &x201C;shadow&x201D; trajectory tracing method to accurately estimate phone&x2019;s and vehicle&x2019;s orientations despite their arbitrary poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides, and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, and collect data with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle&x2019;s real time location with almost negligible latency, with error of <inline-formula><tex-math notation="LaTeX">2\sim 4</tex-math> <alternatives><inline-graphic xlink:href="gao-ieq1-2684167.gif"/></alternatives></inline-formula> parking spaces at the 80th percentile.en_US
dc.format.extent2023,en_US
dc.format.extent2036en_US
dc.publisherIEEEen_US
dc.relation.haspart7880567.pdfen_US
dc.titleSmartphone-Based Real Time Vehicle Tracking in Indoor Parking Structuresen_US
dc.typeArticleen_US
dc.journal.volume16en_US
dc.journal.issue7en_US
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7880567.pdf1.57 MBAdobe PDF