000			01780naaaa2200361uu 4500
001			https://directory.doabooks.org/handle/20.500.12854/50183
005			20220220065131.0
020			_aKSP/1000073704
020			_a9783731507215
024	7		_a10.5445/KSP/1000073704 _cdoi
041	0		_aGerman
042			_adc
100	1		_aRuhhammer, Christian _4auth
245	1	0	_aInferenz von Kreuzungsinformationen aus Flottendaten
260			_bKIT Scientific Publishing _c2017
300			_a1 electronic resource (XIX, 171 p. p.)
506	0		_aOpen Access _2star _fUnrestricted online access
520			_aThe next generation of driver assistance systems and highly automated driving functions are based on digital maps. In order to meet the high requirements on the correctness and up-to-dateness of this information, this work presents new automated methods to extract up-to-date map information from fleet data. The focus is on the inference of static intersection information from fleet data through machine learning and statistical methods.
540			_aCreative Commons _fhttps://creativecommons.org/licenses/by-sa/4.0/ _2cc _4https://creativecommons.org/licenses/by-sa/4.0/
546			_aGerman
653			_aMaschinelles Lernen
653			_aFlottendaten
653			_aIntersection Information
653			_aLichtsignalanlage
653			_aAutomated Map Creation
653			_aFleet Data
653			_aTraffic Light
653			_aAutomatisierte Kartenerstellung
653			_aKreuzungsinformationen
653			_aMachine Learning
856	4	0	_awww.oapen.org _uhttps://www.ksp.kit.edu/9783731507215 _70 _zDOAB: download the publication
856	4	0	_awww.oapen.org _uhttps://directory.doabooks.org/handle/20.500.12854/50183 _70 _zDOAB: description of the publication
999			_c71649 _d71649