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Conference Paper: Pedestrian behavior prediction based on motion patterns for vehicle-to-pedestrian collision avoidance

TitlePedestrian behavior prediction based on motion patterns for vehicle-to-pedestrian collision avoidance
Authors
KeywordsMotion patterns
Pedestrian behaviors
Pedestrian collisions
Prediction methods
Structured environments
Issue Date2008
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000396
Citation
The 11th International IEEE Conference on Intelligent Transportation Systems (ITSC 2008), Beijing, China, 10-12 December 2008. In Conference Proceedings, 2008, p. 316-321 How to Cite?
AbstractThis paper proposes a prediction method for vehicle-to-pedestrian collision avoidance, which learns and then predicts pedestrian behaviors as their motion instances are being observed. During learning, known trajectories are clustered to form Motion Patterns (MP), which become knowledge a priori to a multi-level prediction model that predicts long-term or short-term pedestrian behaviors. Simulation results show that it works well in a complex structured environment and the prediction is consistent with actual behaviors. © 2008 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/61945
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorChen, Zen_HK
dc.contributor.authorNgai, DCKen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2010-07-13T03:50:44Z-
dc.date.available2010-07-13T03:50:44Z-
dc.date.issued2008en_HK
dc.identifier.citationThe 11th International IEEE Conference on Intelligent Transportation Systems (ITSC 2008), Beijing, China, 10-12 December 2008. In Conference Proceedings, 2008, p. 316-321en_HK
dc.identifier.isbn1-4244-2112-1-
dc.identifier.urihttp://hdl.handle.net/10722/61945-
dc.description.abstractThis paper proposes a prediction method for vehicle-to-pedestrian collision avoidance, which learns and then predicts pedestrian behaviors as their motion instances are being observed. During learning, known trajectories are clustered to form Motion Patterns (MP), which become knowledge a priori to a multi-level prediction model that predicts long-term or short-term pedestrian behaviors. Simulation results show that it works well in a complex structured environment and the prediction is consistent with actual behaviors. © 2008 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000396en_HK
dc.relation.ispartofInternational Conference on Intelligent Transportation Proceedingsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_HK
dc.rights©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectMotion patterns-
dc.subjectPedestrian behaviors-
dc.subjectPedestrian collisions-
dc.subjectPrediction methods-
dc.subjectStructured environments-
dc.titlePedestrian behavior prediction based on motion patterns for vehicle-to-pedestrian collision avoidanceen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYung, NHC: nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ITSC.2008.4732644en_HK
dc.identifier.scopuseid_2-s2.0-60749102356en_HK
dc.identifier.hkuros164701en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-60749102356&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage316en_HK
dc.identifier.epage321en_HK
dc.publisher.placeUnited States-
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK
dc.identifier.scopusauthoridNgai, DCK=9332358900en_HK
dc.identifier.scopusauthoridChen, Z=35277857300en_HK
dc.customcontrol.immutablesml 140526-

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