Fusion of infrared vision and radar for estimating the lateral dynamics of obstacles

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dc.contributor.author Amditis, A en
dc.contributor.author Polychronopoulos, A en
dc.contributor.author Floudas, N en
dc.contributor.author Andreone, L en
dc.date.accessioned 2014-03-01T02:43:21Z
dc.date.available 2014-03-01T02:43:21Z
dc.date.issued 2005 en
dc.identifier.issn 15662535 en
dc.identifier.uri http://hdl.handle.net/123456789/31345
dc.subject Collision avoidance en
dc.subject Fusion en
dc.subject Infrared en
dc.subject Kalman filter en
dc.subject Path prediction en
dc.subject Radar en
dc.subject.other Algorithms en
dc.subject.other Collision avoidance en
dc.subject.other Data acquisition en
dc.subject.other Infrared radiation en
dc.subject.other Kalman filtering en
dc.subject.other Motion planning en
dc.subject.other Radar en
dc.subject.other Velocity measurement en
dc.subject.other Adaptive cruise control (ACC) en
dc.subject.other Forward collision warning (FCW) en
dc.subject.other Lateral velocity en
dc.subject.other Path prediction en
dc.subject.other Sensor data fusion en
dc.title Fusion of infrared vision and radar for estimating the lateral dynamics of obstacles en
heal.type conferenceItem en
heal.identifier.primary 10.1016/j.inffus.2004.06.002 en
heal.identifier.secondary http://dx.doi.org/10.1016/j.inffus.2004.06.002 en
heal.publicationDate 2005 en
heal.abstract Automotive forward collision warning systems are based on range finders to detect the obstacles ahead and warn or intervene when a dangerous situation occur. However, the radar information by itself is not adequate to predict the future path of vehicles in collision avoidance systems due to the poor estimation of their lateral attribute. In order to face this problem, this paper proposes the utilization of a new Kalman based filter, whose measurement space includes data from a radar and a vision system. Given the superiority of vision systems in estimating azimuth and lateral velocity, the filter proves to be robust in vehicle maneuvers and curves. Results from simulated and real data are presented, providing comparative results with stand alone tracking systems and the cross-covariance technique in multisensor architectures. © 2004 Elsevier B.V. All rights reserved. en
heal.journalName Information Fusion en
dc.identifier.doi 10.1016/j.inffus.2004.06.002 en
dc.identifier.volume 6 en
dc.identifier.issue 2 en
dc.identifier.spage 129 en
dc.identifier.epage 141 en

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