2024-06-182024-06-182024http://hdl.handle.net/123456789/15858Author ORCID id: 0000-0002-5176-6159In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of University of Kassel's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.engNamensnennung 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/radarautonomous drivingenvironmental perceptionobject detectionclassificationclusteringNLOS imagingmachine learning004380600620Object Detection for Automotive Radar PerceptionDissertationRadarAutonomes FahrzeugUmweltwahrnehmungObjekterkennungVerkehrsteilnehmerKlassifikationCluster <Datenanalyse>Nicht-SichtverbindungMaschinelles LernenMachine Learning Approaches for Identifying Moving Road Users in Urban Scenarios