000			03288cam a2200385Mi 4500
001			on1287131951
003			OCoLC
005			20240726104847.0
008			211204s2021 gw o ||| 0 eng d
040			_aEBLCP _beng _erda _cEBLCP _dYDX _dSFB _dOCLCQ _dOCLCF _dNT
020			_a3736965362
020			_a9783736965362 _q((electronic)l(electronic)ctronic)
050	0	4	_aTL158 _b.S568 2021
049			_aMAIN
100	1		_aEder, Thomas. _e1
245	1	0	_aSimulation of Automotive Radar Point Clouds in Standardized Frameworks
300			_a1 online resource (127 pages)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_adata file _2rda
500			_aDescription based upon print version of record.
504			_a2
505	0	0	_aIntro -- _tChapter 1 Autonomous driving andsimulational challenges -- _t1.1 Safety validation and simulative test drives -- _t1.2 Principles of automotive radar sensors -- _t1.3 Modeling and standardized simulationframeworks -- _tChapter 2 State of research in automotiveradar modeling -- _t2.1 Differentiation of various modeling levels -- _t2.2 Ray-tracing in environments of high-fidelity -- _t2.3 Models executable in standardized environments -- _t2.4 Validation and verification of sensor models -- _tChapter 3 Derivation of research questions,hypotheses and objectives
505	0	0	_a3.2 Stochastic radar models based on deepgenerative networks -- _t3.3 Hybrid multipurpose approaches for radar sensormodels -- _t3.4 Deficiencies of current validation criteria -- _tChapter 4 Modeling challenges related to raycone tracing -- _t4.1 The caustic distance and the angular beamexpansion -- _t4.2 Estimating current errors in case of multiplereflections -- _t4.3 Consequences and lower bounds for the numberof rays -- _tChapter 5 Approaches to statistical radar pointcloud simulation -- _t5.1 Statistical formulation of radar sensor modeling -- _t5.2 Kernel density estimation and radar point clouds
505	0	0	_a5.3 Deep generative networks as sensor models -- _t5.4 Comparison of learning capacities and itsconsequences -- _tChapter 6 A hybrid modeling approach forradar point clouds -- _t6.1 Tracing and catching rays as the baseline -- _t6.2 Improvements to the ray casting approach -- _t6.3 Capabilities for data-based optimization -- _t6.4 Bottom line on the hybrid modeling approach -- _tChapter 7 Validation based on statisticalhypothesis testing -- _t7.1 Consistency of validation criterion -- _t7.2 On the Kolmogorov-Smirnov test -- _t7.3 Applications to radar sensor models
505	0	0	_a7.4 Retrospective and future validation challenges -- _tChapter 8 Conclusion and prospectivechallenges -- _t8.1 Recap of the radar point cloud simulation -- _t8.2 Lessons learned and future recommendations -- _tNomenclatur -- _tReferences -- _tIndex
530			_a2 _ub
650		0	_aCloud computing _xLaw and legislation.
655		1	_aElectronic Books.
856	4	0	_zClick to access digital title | log in using your CIU ID number and my.ciu.edu password. _uhttpss://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=3110287&site=eds-live&custid=s3260518
942			_cOB _D _eEB _hTL _m2021 _QOL _R _x _8NFIC _2LOC
994			_a92 _bNT
999			_c80776 _d80776
902			_a1 _bCynthia Snell _c1 _dCynthia Snell