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Simulation of Automotive Radar Point Clouds in Standardized Frameworks

By: Material type: TextTextDescription: 1 online resource (127 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 3736965362
  • 9783736965362
Subject(s): Genre/Form: LOC classification:
  • TL158 .S568 2021
Online resources: Available additional physical forms:
Contents:
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
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
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
Chapter 8 Conclusion and prospectivechallenges -- 8.1 Recap of the radar point cloud simulation -- 8.2 Lessons learned and future recommendations -- Nomenclatur -- References -- Index
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Holdings
Item type Current library Collection Call number URL Status Date due Barcode
Online Book (LOGIN USING YOUR MY CIU LOGIN AND PASSWORD) Online Book (LOGIN USING YOUR MY CIU LOGIN AND PASSWORD) G. Allen Fleece Library ONLINE Non-fiction TL158 (Browse shelf(Opens below)) Link to resource Available on1287131951

Description based upon print version of record.

Includes bibliographies and index.

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

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

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

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

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