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

Amditis, A; Polychronopoulos, A; Floudas, N; Andreone, L

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	https://dspace.lib.ntua.gr/xmlui/handle/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