Sensor fusion for predicting vehicles' path for collision avoidance systems

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dc.contributor.author Polychronopoulos, A en
dc.contributor.author Tsogas, M en
dc.contributor.author Amditis, AJ en
dc.contributor.author Andreone, L en
dc.date.accessioned 2014-03-01T01:27:15Z
dc.date.available 2014-03-01T01:27:15Z
dc.date.issued 2007 en
dc.identifier.issn 1524-9050 en
dc.identifier.uri http://hdl.handle.net/123456789/18358
dc.subject Collision warning (CW) en
dc.subject Curvilinear motion model en
dc.subject Kalman en
dc.subject Path prediction en
dc.subject Sensor fusion en
dc.subject.classification Engineering, Civil en
dc.subject.classification Engineering, Electrical & Electronic en
dc.subject.classification Transportation Science & Technology en
dc.subject.other Accident prevention en
dc.subject.other Kalman filters en
dc.subject.other Real time systems en
dc.subject.other Traffic control en
dc.subject.other Collision warning (CW) en
dc.subject.other Curvilinear motion models en
dc.subject.other Path prediction en
dc.subject.other Vision enhancement applications en
dc.subject.other Sensor data fusion en
dc.title Sensor fusion for predicting vehicles' path for collision avoidance systems en
heal.type journalArticle en
heal.identifier.primary 10.1109/TITS.2007.903439 en
heal.identifier.secondary http://dx.doi.org/10.1109/TITS.2007.903439 en
heal.language English en
heal.publicationDate 2007 en
heal.abstract Path prediction is the only way that an active safety system can predict a driver's intention. In this paper, a model-based description of the traffic environment is presented-both vehicles and infrastructure-in order to provide, in real time, sufficient information for an accurate prediction of the egovehicle's path. The proposed approach is a hierarchical-structured algorithm that fuses traffic environment data with car dynamics in order to accurately predict the trajectory of the ego-vehicle, allowing the active safety system to inform, warn the driver, or intervene when critical situations occur. The algorithms are tested with real data, under normal conditions, for collision warning (CW) and vision-enhancement applications. The results clearly show that this approach allows a dynamic situation and threat assessment and can enhance the capabilities of adaptive cruise control and CW functions by reducing the false alarm rate. © 2007 IEEE. en
heal.journalName IEEE Transactions on Intelligent Transportation Systems en
dc.identifier.doi 10.1109/TITS.2007.903439 en
dc.identifier.isi ISI:000249403800016 en
dc.identifier.volume 8 en
dc.identifier.issue 3 en
dc.identifier.spage 549 en
dc.identifier.epage 562 en

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