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|a 1287126206
|a 1287200154
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|a 3736965362
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|a 9783736965362
|q (electronic bk.)
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|b 000073248848
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|a (OCoLC)1287131951
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|b .E347 2021
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|2 23
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|a UAMI
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| 100 |
1 |
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|a Eder, Thomas.
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| 245 |
1 |
0 |
|a Simulation of Automotive Radar Point Clouds in Standardized Frameworks
|h [electronic resource].
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| 260 |
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|a Göttingen :
|b Cuvillier Verlag,
|c 2021.
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| 300 |
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|a 1 online resource (127 p.)
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| 500 |
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|a Description based upon print version of record.
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| 505 |
0 |
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|a Intro -- Chapter 1 Autonomous driving andsimulational challenges -- 1.1 Safety validation and simulative test drives -- 1.2 Principles of automotive radar sensors -- 1.3 Modeling and standardized simulationframeworks -- Chapter 2 State of research in automotiveradar modeling -- 2.1 Differentiation of various modeling levels -- 2.2 Ray-tracing in environments of high-fidelity -- 2.3 Models executable in standardized environments -- 2.4 Validation and verification of sensor models -- Chapter 3 Derivation of research questions,hypotheses and objectives
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| 505 |
8 |
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|a 3.2 Stochastic radar models based on deepgenerative networks -- 3.3 Hybrid multipurpose approaches for radar sensormodels -- 3.4 Deficiencies of current validation criteria -- Chapter 4 Modeling challenges related to raycone tracing -- 4.1 The caustic distance and the angular beamexpansion -- 4.2 Estimating current errors in case of multiplereflections -- 4.3 Consequences and lower bounds for the numberof rays -- Chapter 5 Approaches to statistical radar pointcloud simulation -- 5.1 Statistical formulation of radar sensor modeling -- 5.2 Kernel density estimation and radar point clouds
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| 505 |
8 |
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|a 5.3 Deep generative networks as sensor models -- 5.4 Comparison of learning capacities and itsconsequences -- Chapter 6 A hybrid modeling approach forradar point clouds -- 6.1 Tracing and catching rays as the baseline -- 6.2 Improvements to the ray casting approach -- 6.3 Capabilities for data-based optimization -- 6.4 Bottom line on the hybrid modeling approach -- Chapter 7 Validation based on statisticalhypothesis testing -- 7.1 Consistency of validation criterion -- 7.2 On the Kolmogorov-Smirnov test -- 7.3 Applications to radar sensor models
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| 505 |
8 |
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|a 7.4 Retrospective and future validation challenges -- Chapter 8 Conclusion and prospectivechallenges -- 8.1 Recap of the radar point cloud simulation -- 8.2 Lessons learned and future recommendations -- Nomenclatur -- References -- Index
|
| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
|
| 650 |
|
0 |
|a Cloud computing
|x Law and legislation.
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| 650 |
|
7 |
|a Cloud computing
|x Law and legislation
|2 fast
|
| 776 |
0 |
8 |
|i Print version:
|a Eder, Thomas
|t Simulation of Automotive Radar Point Clouds in Standardized Frameworks
|d Göttingen : Cuvillier Verlag,c2021
|z 9783736975361
|
| 856 |
4 |
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|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6820147
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