Sensor fusion for predicting vehicles' path for collision avoidance systems

Polychronopoulos, A; Tsogas, M; Amditis, AJ; Andreone, L

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	https://dspace.lib.ntua.gr/xmlui/handle/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.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	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