Object-Level Fusion for Surround Environment Perception in Automated Driving Applications

Michael Aeberhard

doi:10.51202/9783186804129

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Bibliographic data

Publication year: 2017
Publication date: 25.08.2017
ISBN-Print: 978-3-18-380412-2
ISBN-Online: 978-3-18-680412-9
Publisher: VDI Verlag, Düsseldorf
Series: Verkehrstechnik/Fahrzeugtechnik
Volume: 804
Language: German
Pages: 202
Product type: Book Titles

Titelei/Inhaltsverzeichnis Partial access Pages I - XVI Download chapter (PDF)
1. Motivation No access
2. 1. Level 0 – No Automation No access
  2. Level 1 – Driver Assistance No access
  3. Level 2 – Partial Automation No access
  4. Level 3 – Conditional Automation No access
  5. Levels 4 and 5 – Towards Fully Automated Driving No access
3. Problem of Object Detection No access
4. Contribution and Outline of the Thesis No access
1. 1. Low-Level No access
  2. High-Level No access
  3. Hybrid No access
  4. Comparison No access
2. 1. Object Model No access
  2. Sensor-Level No access
  3. Fusion-Level No access
  4. Application-Level No access
1. 1. Spatial No access
  2. Temporal No access
2. 1. Sensor-to-Sensor No access
  2. Sensor-to-Global No access
3. 1. Architecture No access
  2. Feature Selection No access
  3. State Vector No access
  4. Geometrical No access
  5. Association Validation No access
  6. Multi-Object Association No access
1. 1. Feature Extraction No access
  2. Data Association No access
  3. Filtering No access
  4. TrackManagement No access
  5. KinematicModels No access
2. 1. Process Noise No access
  2. Common Information History No access
  3. Out-of-Sequence Data No access
3. 1. Adapted Kalman Filter No access
  2. Covariance Intersection No access
  3. InformationMatrix Fusion No access
  4. Comparison No access
4. 1. Dimension Estimation No access
  2. Extraction of Fused Coordinates No access
1. 1. Existence Prediction No access
  2. Existence Update No access
  3. Generalized Bayes Extension No access
  4. Modeling the Parameters No access
  5. Object Management No access
2. 1. Architecture No access
  2. Modeling with Dempster-Shafer Evidence Theory No access
  3. Extension for Occlusion Modeling No access
  4. Modeling the Trust Probability No access
1. 1. Measurement Classification No access
  2. Temporal Filtering No access
2. 1. Modeling with the Dempster-Shafer Evidence Theory No access
  2. Modeling the Trust Probability No access
1. Test Vehicle and Sensor Configuration No access
2. 1. Ground Truth Calculation No access
  2. State Estimation No access
  3. Existence No access
  4. Classification No access
3. 1. Detection Rate No access
  2. Classification Performance No access
  3. Integration in Automated Driving and ADAS Projects No access
Conclusion and Discussion No access Pages 152 - 154
1. Simple Weighted Fusion No access
2. Use of Cross-Covariance No access
3. Covariance Intersection No access
4. Comparison No access
Information Matrix Fusion Derivation No access Pages 162 - 163
1. Existence No access
2. Classification No access
1. Training Data No access
2. 1. Test Track No access
  2. Real Traffic No access
References No access Pages 172 - 202