Lateral motion tracking of automobiles

Amditis, A; Floudas, N; Polychronopoulos, A

dc.contributor.author	Amditis, A	en
dc.contributor.author	Floudas, N	en
dc.contributor.author	Polychronopoulos, A	en
dc.date.accessioned	2014-03-01T02:49:47Z
dc.date.available	2014-03-01T02:49:47Z
dc.date.issued	2004	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34747
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-6344291418&partnerID=40&md5=f27d809a736501670fc0416e7684df62	en
dc.subject	Collision Avoidance	en
dc.subject	Fusion	en
dc.subject	Infrared	en
dc.subject	Kalman	en
dc.subject	Path prediction	en
dc.subject	Radar	en
dc.subject.other	Adaptive control systems	en
dc.subject.other	Collision avoidance	en
dc.subject.other	Computer simulation	en
dc.subject.other	Estimation	en
dc.subject.other	Infrared radiation	en
dc.subject.other	Kalman filtering	en
dc.subject.other	Matrix algebra	en
dc.subject.other	Radar	en
dc.subject.other	Sensors	en
dc.subject.other	Tracking (position)	en
dc.subject.other	Vision	en
dc.subject.other	Adaptive cruise control (ACC)	en
dc.subject.other	Kalman	en
dc.subject.other	Path prediction	en
dc.subject.other	Automobiles	en
dc.title	Lateral motion tracking of automobiles	en
heal.type	conferenceItem	en
heal.publicationDate	2004	en
heal.abstract	Radar sensors, though successful in range parameters tracking, fall short in lateral characteristics tracking. On the other hand, vision systems carry out perfect estimation for lateral motion, but range parameters estimation does not surpass the performance of radar. The exact estimation of vehicle's motion characteristics appears to be a crucial issue in modern automobile collision avoidance systems. Thus, a fusion system comprising of a radar and a FLIR could offer an overall accurate estimation for targets moving in highways. The main scope of the paper is the presentation of a double Kalman based filter which strives in lateral motion estimation mainly. The performance of the filter is tested by means of simulated data sets and compared with the results of the popular fusion method of cross-covariance matrix and the single sensor tracking for each sensor.	en
heal.journalName	Proceedings of the Seventh International Conference on Information Fusion, FUSION 2004	en
dc.identifier.volume	2	en
dc.identifier.spage	768	en
dc.identifier.epage	774	en