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BL
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| Record Number
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871396
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| Main Entry
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Internationale ATZ-Fachtagung Automatisiertes Fahren(4th :2018 :, Wiesbaden, Germany)
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| Title & Author
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Fahrerassistenzsysteme 2018 : : von der Assistenz zum automatisierten Fahren ; 4. Internationale ATZ-Fachtagung Automatisiertes Fahren /\ Hrsg. Torsten Bertram.
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| Publication Statement
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Wiesbaden, Germany :: Springer Vieweg,, [2019]
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| Series Statement
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Proceedings,
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| Page. NO
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1 online resource
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| ISBN
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3658237511
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: 9783658237516
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3658237503
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9783658237509
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Intro; Vorwort; Inhaltsverzeichnis; Radar for Autonomous Driving -- Paradigm Shift from Mere Detection to Semantic Environment Understanding; 1 Introduction; 1.1 The History of Automotive Radar Answers the Question: "Why Radar?"; 1.2 Paradigm-Shift in Development Guidelines: Deep Learning and Model-Based Approaches Together on Imaging Like Radar Data; 1.3 Is Deep Learning Really the Answer to Everything?; 2 Radar Application for Dynamic Objects; 2.1 Classification; 2.2 Extended Target Tracking; 2.3 Classification Assisted Tracking; 3 Scene Understanding of Non-moving Targets
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1.2 Standardization of the Logical Interface from Sensors to the Fusion Unit1.3 Status of the Current VDA Initiative to Standardize Sensor Interfaces in ISO; 2 Standardized Interfaces in the Context of Virtual Test and Validation; 3 Conclusion; References; Virtualization for Verifying Functional Safety of Highly Automated Driving Using the Example of a Real ECU Project; Abstract.; 1 Introduction; 2 Legal Safety Requirements; 3 ECU Virtualization for HAD Testing; 3.1 Advantages of ECU Virtualization; 3.2 Challenges of ECU Virtualization; 3.3 Virtualization Approach for HAD ECUs
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3.1 Semantic Radar Grids3.2 Road Course Estimation; 4 Future Trend: High Resolution Radar Combined with Artificial Intelligence; 5 Conclusion; References; Improving the Environment Model for Highly Automated Driving by Extending the Sensor Range; Abstract.; 1 Introduction; 2 Current Approaches; 3 Simultaneous Localization And Mapping (SLAM); 3.1 Particle Distribution Logic -- General Idea; 3.2 Particle Distribution Areas; 3.3 Jump Suppression; 4 Cloud-Based Technology; 4.1 Utilizing the Global Grid Layer; 4.2 Graph-Based SLAM Algorithm in the Cloud; 4.3 Visualization as a Service; 5 Results
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5.1 Increase Confidence of Position5.2 Global Correction of Local Errors; 5.3 Initialization of Local Grid Maps by Cloud-Based Environment Model Tiles; 5.4 Scalable and Secure Web Service; 5.5 Benefits for Data Analysts and Developers; 6 Conclusion and Outlook; References; Efficient Sensor Development Using Raw Signal Interfaces; 1 Introduction; 2 Classification of Sensor Models; 2.1 Ideal Sensor Models; 2.2 Phenomenological Sensor Models; 2.3 Physical Modelling of Signal Propagation Using a Raw Signal Interface; 3 Functional Principle of a Radar Sensor; 4 Radar RSI; 5 Application Example
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6 OutlookReferences; 360° Surround View Radar for Driver Assistance and Automated Driving; 1 Evolving Market Regarding Automated Driving; 2 From Driver Assistance to Automated Driving; 3 Radar Sensors for Automated Driving; 4 Radar Based Environment Perception for Automated Parking; 5 Intelligent Validation of Automated Driving Features; 6 Partnerships as Key to Accelerate Development; 7 Summary and Outlook; Overall Approach to Standardize AD Sensor Interfaces: Simulation and Real Vehicle; Abstract.; 1 Motivation for Sensor Interface Standardization; 1.1 Complex Sensor Setups for AD Functions
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| Subject
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Driver assistance systems, Congresses.
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| Subject
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Driver assistance systems.
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| Subject
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TECHNOLOGY ENGINEERING-- Engineering (General)
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| Dewey Classification
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629.283
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| LC Classification
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TL272.57
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| Added Entry
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Bertram, Torsten
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