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dc.contributor.author정호기-
dc.date.accessioned2017-10-24T06:06:32Z-
dc.date.available2017-10-24T06:06:32Z-
dc.date.issued2015-12-
dc.identifier.citationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v. 16, NO 6, Page. 3377-3392en_US
dc.identifier.issn1524-9050-
dc.identifier.issn1558-0016-
dc.identifier.urihttp://ieeexplore.ieee.org/document/7160754/-
dc.identifier.urihttp://hdl.handle.net/20.500.11754/30230-
dc.description.abstractThis paper presents a Monte Carlo localization algorithm for an autonomous car based on an integration of multiple sensors data. The sensor system is composed of onboard motion sensors, a low-cost GPS receiver, a precise digital map, and multiple cameras. Data from the onboard motion sensors, such as yaw rate and wheel speeds, are used to predict the vehicle motion, and the GPS receiver is applied to establish the validation boundary of the ego-vehicle position. The digital map contains location information at the centimeter level about road surface markers (RSMs), such as lane markers, stop lines, and traffic sign markers. The multiple images from the front and rear mono-cameras and the around-view monitoring system are used to detect the RSM features. The localization algorithm updates the measurements by matching the RSM features from the cameras to the digital map based on a particle filter. Because the particle filter updates the measurements based on a probabilistic sensor model, the exact probabilistic modeling of sensor noise is a key factor to enhance the localization performance. To design the probabilistic noise model of the RSM features more explicitly, we analyze the results of the RSM feature detection for various real driving conditions. The proposed localization algorithm is verified and evaluated through experiments under various test scenarios and configurations. From the experimental results, we conclude that the presented localization algorithm based on the probabilistic noise model of RSM features provides sufficient accuracy and reliability for autonomous driving system applications.en_US
dc.description.sponsorshipThis work was supported in part by an NRF grant funded by the Korean government (MEST) (No. 2011-0017495), by the Industrial Strategy Technology Development Program of MKE (No. 10039673), and by the BK21 plus program (22A20130000045) under the Ministry of Education, Republic of Korea. The Associate Editor for this paper was N. Zheng.en_US
dc.language.isoenen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.subjectPrecise localizationen_US
dc.subjectmultiple camerasen_US
dc.subjectroad surface markeren_US
dc.subjectprobabilistic noise modelingen_US
dc.subjectprobabilistic noise model of road surface marker (RSM) featuresen_US
dc.subjectparticle filteringen_US
dc.subjectautonomous caren_US
dc.subjectautonomous drivingen_US
dc.titlePrecise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Camerasen_US
dc.typeArticleen_US
dc.relation.no6-
dc.relation.volume16-
dc.identifier.doi10.1109/TITS.2015.2450738-
dc.relation.page3377-3392-
dc.relation.journalIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS-
dc.contributor.googleauthorJo, Kichun-
dc.contributor.googleauthorJo, Yongwoo-
dc.contributor.googleauthorSuhr, Jae Kyu-
dc.contributor.googleauthorJung, Ho Gi-
dc.contributor.googleauthorSunwoo, Myoungho-
dc.relation.code2015001249-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF AUTOMOTIVE ENGINEERING-
dc.identifier.pidhogijung-
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COLLEGE OF ENGINEERING[S](공과대학) > AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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