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Bayesian Environment Representation, Prediction, and Criticality Assessment for Driver Assistance Systems / Bibliography
Bayesian Environment Representation, Prediction, and Criticality Assessment for Driver Assistance Systems / Bibliography
Contents
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Titelei/Inhaltsverzeichnis
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1–5
1 Introduction
1–5
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1.1 Contributions
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1.2 Thesis Structure
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6–17
2 Environment Representations for ADAS
6–17
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2.1 Introduction and Motivation
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2.2 Related Work
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2.3 Proposed Representation
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18–66
3 Bayesian Inference for Nonlinear Filtering Tasks
18–66
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3.1 Bayesian Filtering
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3.1.1 Optimal Bayesian Filter
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3.1.2 Kalman Filter
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3.1.3 Information Filter
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3.1.4 Extended Kalman Filter
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3.1.5 Unscented Kalman Filter
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3.1.6 Multiple Model Optimal Bayesian Filter
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3.1.7 Interacting Multiple Model Filter
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3.1.8 Binary Bayes Filter
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3.2 Target Tracking
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3.2.1 Tracking Filters and Data Association
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3.2.2 Probabilistic Data Association Filter
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3.2.3 IMM-UK-PDAF
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3.3 Occupancy Grid Mapping
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3.3.1 Traditional Mapping Solution
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3.3.2 Inverse Sensor Models
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67–102
4 Grid Mapping in Dynamic Environments
67–102
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4.1 Introduction and Motivation
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4.2 Related Work
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4.3 Proposed Approach
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4.3.1 System Overview
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4.3.2 Generation of Dynamic Cell Hypothesis
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4.3.3 Tracking of Dynamic Cell Hypothesis
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4.3.4 Classification and Grid Post Processing
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4.4 Implementation and Performance
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4.5 Evaluation
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4.5.1 Track Level Evaluation
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4.5.2 Cell Level Evaluation
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4.6 Conclusion
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103–131
5 Parametric Free Space Maps
103–131
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5.1 Introduction and Motivation
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5.2 Related Work
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5.3 Generation of Parametric Free Space Maps
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5.3.1 System Overview
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5.3.2 Free Space Detection by Grid Map Image Analysis
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5.3.3 Dynamic B-Spline Free Space Contour Tracking
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5.3.4 Description of Inner Free Space Boundaries
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5.4 Implementation and Performance
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5.5 Experimental Results
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5.6 Conclusion
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132–191
6 Prediction and Criticality Assessment
132–191
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6.1 Introduction and Motivation
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6.2 Related Work
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6.2.1 Short-Term Trajectory Prediction
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6.2.2 Long-Term Trajectory Prediction
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6.2.3 Situation Recognition and Prediction
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6.2.4 Criticality Assessment
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6.3 Proposed Approach
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6.3.1 System Overview
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6.3.2 Maneuver Detection with Bayesian Networks
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6.3.3 Maneuver-Based, Long-Term Trajectory Prediction
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6.3.4 Criticality Assessment
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6.4 Simulation Environment and Results
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6.4.1 Dangerous Lane Change Scenario
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6.4.2 Near-Collision Turn Scenario
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6.4.3 Static Environment Collision Scenario
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6.5 Conclusion
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192–206
7 PRORETA 3: An Integrated Driver Assistance System
192–206
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7.1 Introduction and Motivation
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7.2 SystemOverview
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7.2.1 Software Architecture
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7.2.2 Environment Representation and Planning
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7.2.3 Human Machine Interface
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7.3 Driving Scenarios and Results
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7.4 Conclusion
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207–211
8 Summary and Outlook
207–211
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212–216
A Bayesian Network Parameters
212–216
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217–220
B Publications and Supervisions
217–220
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B.1 List of Publications by the Author
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B.1.1 Journal and Book Chapter Publications
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B.1.2 Conference Publications
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B.2 List of Supervisions by the Author
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221–254
Bibliography
221–254
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Bayesian Environment Representation, Prediction, and Criticality Assessment for Driver Assistance Systems , page 221 - 254
Bibliography
Autoren
Matthias Schreiner
DOI
doi.org/10.51202/9783186797124-221
ISBN print: 978-3-18-379712-7
ISBN online: 978-3-18-679712-4
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doi.org/10.51202/9783186797124-221
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