Bayesian Environment Representation, Prediction, and Criticality Assessment for Driver Assistance Systems

Matthias Schreiner

doi:10.51202/9783186797124

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Bibliographic data

Copyright year: 2016
Publication date: 29.02.2016
ISBN-Print: 978-3-18-379712-7
ISBN-Online: 978-3-18-679712-4
Publisher: VDI Verlag, Düsseldorf
Series: Verkehrstechnik/Fahrzeugtechnik
Volume: 797
Language: German
Pages: 254
Product type: Book Titles

Titelei/Inhaltsverzeichnis Partial access Pages I - XXII Download chapter (PDF)
1. Contributions No access
2. Thesis Structure No access
1. Introduction and Motivation No access
2. Related Work No access
3. Proposed Representation No access
1. 1. Optimal Bayesian Filter No access
  2. Kalman Filter No access
  3. Information Filter No access
  4. Extended Kalman Filter No access
  5. Unscented Kalman Filter No access
  6. Multiple Model Optimal Bayesian Filter No access
  7. Interacting Multiple Model Filter No access
  8. Binary Bayes Filter No access
2. 1. Tracking Filters and Data Association No access
  2. Probabilistic Data Association Filter No access
  3. IMM-UK-PDAF No access
3. 1. Traditional Mapping Solution No access
  2. Inverse Sensor Models No access
1. Introduction and Motivation No access
2. Related Work No access
3. 1. System Overview No access
  2. Generation of Dynamic Cell Hypothesis No access
  3. Tracking of Dynamic Cell Hypothesis No access
  4. Classification and Grid Post Processing No access
4. Implementation and Performance No access
5. 1. Track Level Evaluation No access
  2. Cell Level Evaluation No access
6. Conclusion No access
1. Introduction and Motivation No access
2. Related Work No access
3. 1. System Overview No access
  2. Free Space Detection by Grid Map Image Analysis No access
  3. Dynamic B-Spline Free Space Contour Tracking No access
  4. Description of Inner Free Space Boundaries No access
4. Implementation and Performance No access
5. Experimental Results No access
6. Conclusion No access
1. Introduction and Motivation No access
2. 1. Short-Term Trajectory Prediction No access
  2. Long-Term Trajectory Prediction No access
  3. Situation Recognition and Prediction No access
  4. Criticality Assessment No access
3. 1. System Overview No access
  2. Maneuver Detection with Bayesian Networks No access
  3. Maneuver-Based, Long-Term Trajectory Prediction No access
  4. Criticality Assessment No access
4. 1. Dangerous Lane Change Scenario No access
  2. Near-Collision Turn Scenario No access
  3. Static Environment Collision Scenario No access
5. Conclusion No access
1. Introduction and Motivation No access
2. 1. Software Architecture No access
  2. Environment Representation and Planning No access
  3. Human Machine Interface No access
3. Driving Scenarios and Results No access
4. Conclusion No access
Summary and Outlook No access Pages 207 - 211
Bayesian Network Parameters No access Pages 212 - 216
1. 1. Journal and Book Chapter Publications No access
  2. Conference Publications No access
2. List of Supervisions by the Author No access
Bibliography No access Pages 221 - 254