von der Assistenz zum automatisierten Fahren ; 4. Internationale ATZ-Fachtagung Automatisiertes Fahren /
Hrsg. Torsten Bertram.
Wiesbaden, Germany :
Springer Vieweg,
[2019]
1 online resource
Proceedings,
2198-7432
Includes bibliographical references and index.
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
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
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
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
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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Springer Nature
com.springer.onix.9783658237516
Fahrerassistenzsysteme 2018.
9783658237509
Driver assistance systems, Congresses.
Driver assistance systems.
TECHNOLOGY & ENGINEERING-- Engineering (General)
TEC-- 009000
629
.
283
23
TL272
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57
Bertram, Torsten
Internationale ATZ-Fachtagung Automatisiertes Fahren(4th :2018 :, Wiesbaden, Germany)