Multiple hypothesis tracking for automated vehicle perception

Thomaidis, G; Spinoulas, L; Lytrivis, P; Ahrholdt, M; Grubb, G; Amditis, A

dc.contributor.author	Thomaidis, G	en
dc.contributor.author	Spinoulas, L	en
dc.contributor.author	Lytrivis, P	en
dc.contributor.author	Ahrholdt, M	en
dc.contributor.author	Grubb, G	en
dc.contributor.author	Amditis, A	en
dc.date.accessioned	2014-03-01T02:46:53Z
dc.date.available	2014-03-01T02:46:53Z
dc.date.issued	2010	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32914
dc.subject	Laser Scanner	en
dc.subject	Multiple Hypothesis Tracking	en
dc.subject	Target Tracking	en
dc.subject	Vision System	en
dc.subject.other	Advanced vehicle	en
dc.subject.other	Automated driving	en
dc.subject.other	Automated vehicles	en
dc.subject.other	Automotive sensors	en
dc.subject.other	Multi sensor	en
dc.subject.other	Multiple hypothesis tracking	en
dc.subject.other	Multitarget	en
dc.subject.other	Performance benefits	en
dc.subject.other	Real sensor data	en
dc.subject.other	Tracking performance	en
dc.subject.other	Transport safety	en
dc.subject.other	Vision systems	en
dc.subject.other	Automation	en
dc.subject.other	Intelligent vehicle highway systems	en
dc.subject.other	Sensors	en
dc.subject.other	Target tracking	en
dc.subject.other	Vehicles	en
dc.title	Multiple hypothesis tracking for automated vehicle perception	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/IVS.2010.5548070	en
heal.identifier.secondary	5548070	en
heal.identifier.secondary	http://dx.doi.org/10.1109/IVS.2010.5548070	en
heal.publicationDate	2010	en
heal.abstract	The use of multiple hypothesis tracking has proven to provide significant performance benefits over the single hypothesis GNN or the PDA algorithm. Automotive sensors like radars, laser-scanners or vision systems are being integrated into vehicles for commercial or scientific purposes, in increasing numbers over the last years. As a result, there is profound literature on this area and several approaches have been proposed to the problem of multi-target, multi-sensor target tracking. The most advanced vehicle applications allow the use of highly or even fully automated driving. Of course, these applications require an accurate, robust and reliable perception output so that the vehicle can be driven autonomously. The HAVEit EU project investigates the application and validation of automated vehicles applications, technologies that are going to have great impact in transport safety and comfort. In this paper the MHT algorithm is applied to real sensor data, installed in Volvo Technology vehicle demonstrating Automated Queue Assistance. In conjunction with simulated scenarios, the benefits in tracking performance compared to conventional GNN tracking are presented. © 2010 IEEE.	en
heal.journalName	IEEE Intelligent Vehicles Symposium, Proceedings	en
dc.identifier.doi	10.1109/IVS.2010.5548070	en
dc.identifier.spage	1222	en
dc.identifier.epage	1227	en